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unsloth/studio/backend/core/inference/llama_cpp.py
Leo Borcherding efed5c3739
fix(studio): use endswith for mmproj F16 variant selection (#5184) 
"f16" in filename matched BF16 files because "bf16" contains "f16"
as a substring. Switch to endswith("-f16.gguf") for an exact match.
2026-04-25 16:49:05 -07:00

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# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
llama-server inference backend for GGUF models.
Manages a llama-server subprocess and proxies chat completions
through its OpenAI-compatible /v1/chat/completions endpoint.
"""
import atexit
import contextlib
import json
import re
import struct
import structlog
from loggers import get_logger
import shutil
import socket
import subprocess
import sys
import threading
import time
from pathlib import Path
from typing import Generator, Optional
from urllib.parse import urlparse
import httpx
from utils.subprocess_compat import (
windows_hidden_subprocess_kwargs as _windows_hidden_subprocess_kwargs,
)
logger = get_logger(__name__)
# ── Pre-compiled patterns for plan-without-action re-prompt ──
# Forward-looking intent signals that indicate the model is
# describing what it *will* do rather than giving a final answer.
_INTENT_SIGNAL = re.compile(
r"(?i)("
# Direct intent: "I'll ...", "I will ...", "Let me ...", "I am going to ..."
# Handles both straight and curly apostrophes.
# Excludes "I can", "I should", "I want to", "let's" which
# appear frequently in direct answers / explanations.
r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b"
r"|"
# Step/plan framing: "First ...", "Step 1:", "Here's my plan"
r"\b(?:first\b|step \d+:?|here['\u2019]?s (?:my |the |a )?(?:plan|approach))"
r"|"
# "Now I" / "Next I" patterns
r"\b(?:now i|next i)\b"
r")"
)
_MAX_REPROMPTS = 3
# Without max_tokens, llama-server defaults to n_predict = n_ctx (up to
# 262144 for Qwen3.5), producing many-minute zombie decodes when cancel
# fails. t_max_predict_ms is a wall-clock backstop applied unconditionally,
# but the llama.cpp README notes it ONLY fires after a newline has been
# generated -- a model stuck in a long unbroken non-newline sequence is
# unbounded by it. So we still want a token cap as the front-line limiter.
#
# The cap is the model's effective context length when we know it,
# falling back to a generous floor when metadata is unavailable. 4096 was
# too low: Qwen3 / gpt-oss reasoning traces routinely exceed it, and any
# OpenAI-API caller that omits max_tokens (langchain, llama-index, raw
# curl) sees responses silently truncated mid-sentence.
_DEFAULT_MAX_TOKENS_FLOOR = 32768
_DEFAULT_T_MAX_PREDICT_MS = 600_000 # 10 min
_REPROMPT_MAX_CHARS = 2000
# ── Pre-compiled patterns for GGUF shard detection ───────────
_SHARD_FULL_RE = re.compile(r"^(.*)-(\d{5})-of-(\d{5})\.gguf$")
_SHARD_RE = re.compile(r"^(.*)-\d{5}-of-\d{5}\.gguf$")
# Model size extraction — lazy import to avoid pulling in transformers
# at module level. See PR description for the full explanation.
def _extract_model_size_b(model_id: str):
from utils.models import extract_model_size_b
return extract_model_size_b(model_id)
# ── Pre-compiled patterns for tool XML stripping ─────────────
_TOOL_CLOSED_PATS = [
re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL),
re.compile(r"<function=\w+>.*?</function>", re.DOTALL),
]
_TOOL_ALL_PATS = _TOOL_CLOSED_PATS + [
re.compile(r"<tool_call>.*$", re.DOTALL),
re.compile(r"<function=\w+>.*$", re.DOTALL),
]
# ── Pre-compiled patterns for tool-call XML parsing ──────────
_TC_JSON_START_RE = re.compile(r"<tool_call>\s*\{")
_TC_FUNC_START_RE = re.compile(r"<function=(\w+)>\s*")
_TC_END_TAG_RE = re.compile(r"</tool_call>")
_TC_FUNC_CLOSE_RE = re.compile(r"\s*</function>\s*$")
_TC_PARAM_START_RE = re.compile(r"<parameter=(\w+)>\s*")
_TC_PARAM_CLOSE_RE = re.compile(r"\s*</parameter>\s*$")
_TOOL_TEMPLATE_MARKERS = (
"{%- if tools %}",
"{%- if tools -%}",
"{% if tools %}",
"{% if tools -%}",
'"role" == "tool"',
"'role' == 'tool'",
'message.role == "tool"',
"message.role == 'tool'",
)
def detect_reasoning_flags(
chat_template: Optional[str],
model_identifier: Optional[str] = None,
*,
log_source: Optional[str] = None,
) -> dict:
"""Classify a chat template's reasoning and tool-calling capabilities.
Returns a dict with the same five keys populated by the GGUF sniffer:
``supports_reasoning``, ``reasoning_style``
(``"enable_thinking"`` | ``"reasoning_effort"``),
``reasoning_always_on``, ``supports_preserve_thinking``, and
``supports_tools``. Used by both the llama-server backend at load
time and the safetensors/transformers paths in ``routes/inference``
so the two agree on what the frontend will see.
"""
flags = {
"supports_reasoning": False,
"reasoning_style": "enable_thinking",
"reasoning_always_on": False,
"supports_preserve_thinking": False,
"supports_tools": False,
}
if not chat_template:
return flags
tpl = chat_template
prefix = f"{log_source}: " if log_source else ""
if "enable_thinking" in tpl:
flags["supports_reasoning"] = True
flags["reasoning_style"] = "enable_thinking"
logger.info(f"{prefix}model supports reasoning (enable_thinking)")
elif "reasoning_effort" in tpl:
# gpt-oss / Harmony templates use reasoning_effort
# ("low" | "medium" | "high") instead of a boolean.
flags["supports_reasoning"] = True
flags["reasoning_style"] = "reasoning_effort"
logger.info(f"{prefix}model supports reasoning (reasoning_effort)")
elif "thinking" in tpl:
# DeepSeek uses 'thinking' instead of 'enable_thinking'
normalized_id = (model_identifier or "").lower()
if "deepseek" in normalized_id:
flags["supports_reasoning"] = True
logger.info(f"{prefix}model supports reasoning (DeepSeek thinking)")
# Hardcoded <think> tags or reasoning_content in the template mean
# thinking is always on (no toggle to disable it).
if not flags["supports_reasoning"]:
if ("<think>" in tpl and "</think>" in tpl) or "reasoning_content" in tpl:
flags["supports_reasoning"] = True
flags["reasoning_always_on"] = True
logger.info(f"{prefix}model always reasons (<think> tags in template)")
# preserve_thinking is an independent kwarg on some Qwen templates
# that keeps historical <think> blocks in prior assistant turns.
if "preserve_thinking" in tpl:
flags["supports_preserve_thinking"] = True
logger.info(f"{prefix}model supports preserve_thinking")
if any(marker in tpl for marker in _TOOL_TEMPLATE_MARKERS):
flags["supports_tools"] = True
logger.info(f"{prefix}model supports tool calling")
return flags
class LlamaCppBackend:
"""
Manages a llama-server subprocess for GGUF model inference.
Lifecycle:
1. load_model() — starts llama-server with the GGUF file
2. generate_chat_completion() — proxies to /v1/chat/completions, streams back
3. unload_model() — terminates llama-server subprocess
"""
def __init__(self):
self._process: Optional[subprocess.Popen] = None
self._port: Optional[int] = None
self._model_identifier: Optional[str] = None
self._gguf_path: Optional[str] = None
self._hf_repo: Optional[str] = None
self._hf_variant: Optional[str] = None
self._is_vision: bool = False
self._healthy = False
self._context_length: Optional[int] = None
self._effective_context_length: Optional[int] = None
self._max_context_length: Optional[int] = None
self._chat_template: Optional[str] = None
self._supports_reasoning: bool = False
self._reasoning_always_on: bool = False
self._reasoning_style: str = "enable_thinking"
self._supports_preserve_thinking: bool = False
self._supports_tools: bool = False
self._cache_type_kv: Optional[str] = None
self._reasoning_default: bool = True
self._speculative_type: Optional[str] = None
# KV-cache estimation fields (populated by _read_gguf_metadata)
self._n_layers: Optional[int] = None
self._n_kv_heads: Optional[int] = None
self._n_heads: Optional[int] = None
self._embedding_length: Optional[int] = None
# Architecture-aware KV fields (8 new fields for 5-path estimation)
self._kv_key_length: Optional[int] = None
self._kv_value_length: Optional[int] = None
self._sliding_window: Optional[int] = None
self._full_attention_interval: Optional[int] = None
self._kv_lora_rank: Optional[int] = None
self._key_length_mla: Optional[int] = None
self._ssm_inner_size: Optional[int] = None
self._ssm_state_size: Optional[int] = None
self._lock = threading.Lock()
self._stdout_lines: list[str] = []
self._stdout_thread: Optional[threading.Thread] = None
self._cancel_event = threading.Event()
self._api_key: Optional[str] = None
self._kill_orphaned_servers()
atexit.register(self._cleanup)
# ── Properties ────────────────────────────────────────────────
@property
def is_loaded(self) -> bool:
return self._process is not None and self._healthy
@property
def is_active(self) -> bool:
"""True if a llama-server process exists (loading or loaded)."""
return self._process is not None
@property
def base_url(self) -> str:
return f"http://127.0.0.1:{self._port}"
@property
def model_identifier(self) -> Optional[str]:
return self._model_identifier
@property
def is_vision(self) -> bool:
return self._is_vision
@property
def hf_variant(self) -> Optional[str]:
return self._hf_variant
@property
def context_length(self) -> Optional[int]:
"""Return the effective context length the server is running at."""
return self._effective_context_length or self._context_length
@property
def max_context_length(self) -> Optional[int]:
"""Return the largest context that fits on this hardware at load time.
This is the "safe zone" threshold the UI renders warnings
against. For a model whose weights fit on some GPU subset, it
is the binary-search cap from ``_fit_context_to_vram`` for that
subset. For a model whose weights exceed 90% of every GPU
subset, it is the 4096 fallback -- the spec's default when the
model will not fit. The UI slider ceiling is
``native_context_length``; dragging above ``max_context_length``
triggers the "might be slower" warning.
"""
return self._max_context_length or self._context_length
@property
def native_context_length(self) -> Optional[int]:
"""Return the model's native context length from GGUF metadata."""
return self._context_length
def load_progress(self) -> Optional[dict]:
"""Return live model-load progress, or None if not loading.
While llama-server is warming up, its process is typically in
kernel state D (disk sleep) mmap'ing the weight shards into
page cache before pushing layers to VRAM. During that window
``/api/inference/status`` only reports ``loading``, which gives
the UI nothing to display besides a spinner that looks stuck
for minutes on large MoE models.
This method samples ``/proc/<pid>/status VmRSS`` against the
sum of the GGUF shard sizes so the UI can render a real bar
and compute rate / ETA. Returns ``None`` when no load is in
flight (no process, or process already healthy).
Shape::
{
"phase": "mmap" | "ready",
"bytes_loaded": int, # VmRSS of the llama-server
"bytes_total": int, # sum of shard file sizes
"fraction": float, # bytes_loaded / bytes_total, 0..1
}
Linux-only in the current implementation. On macOS/Windows the
equivalent would be a different API; this returns ``None`` on
platforms where ``/proc/<pid>/status`` is unavailable.
"""
proc = self._process
if proc is None:
return None
pid = proc.pid
if pid is None:
return None
# Sum up shard sizes (primary + any extras sitting alongside).
bytes_total = 0
gguf_path = self._gguf_path
if gguf_path:
primary = Path(gguf_path)
try:
if primary.is_file():
bytes_total += primary.stat().st_size
except OSError:
pass
# Extra shards live alongside the primary with the same prefix
# before the shard index (e.g. ``-00001-of-00004.gguf``).
try:
parent = primary.parent
stem = primary.name
m = _SHARD_RE.match(stem)
prefix = m.group(1) if m else None
if prefix and parent.is_dir():
for sibling in parent.iterdir():
if (
sibling.is_file()
and sibling.name.startswith(prefix)
and sibling.name != stem
and sibling.suffix == ".gguf"
):
try:
bytes_total += sibling.stat().st_size
except OSError:
pass
except OSError:
pass
# Read VmRSS from /proc/<pid>/status. Kilobytes on Linux.
bytes_loaded = 0
try:
with open(f"/proc/{pid}/status", "r", encoding = "utf-8") as f:
for line in f:
if line.startswith("VmRSS:"):
kb = int(line.split()[1])
bytes_loaded = kb * 1024
break
except (FileNotFoundError, PermissionError, ValueError, OSError):
return None
phase = "ready" if self._healthy else "mmap"
fraction = 0.0
if bytes_total > 0:
fraction = min(1.0, bytes_loaded / bytes_total)
return {
"phase": phase,
"bytes_loaded": bytes_loaded,
"bytes_total": bytes_total,
"fraction": round(fraction, 4),
}
@property
def chat_template(self) -> Optional[str]:
return self._chat_template
@property
def supports_reasoning(self) -> bool:
return self._supports_reasoning
@property
def reasoning_always_on(self) -> bool:
return self._reasoning_always_on
@property
def reasoning_style(self) -> str:
return self._reasoning_style
@property
def supports_preserve_thinking(self) -> bool:
return self._supports_preserve_thinking
@property
def reasoning_default(self) -> bool:
return self._reasoning_default
def _reasoning_kwargs(self, enable_thinking: bool) -> dict:
if self._reasoning_style == "reasoning_effort":
return {"reasoning_effort": "high" if enable_thinking else "low"}
return {"enable_thinking": enable_thinking}
def _request_reasoning_kwargs(
self,
enable_thinking: Optional[bool],
reasoning_effort: Optional[str] = None,
preserve_thinking: Optional[bool] = None,
) -> Optional[dict]:
"""Build chat_template_kwargs from per-request reasoning fields.
Produces a merged dict covering the active model's reasoning style
(``enable_thinking`` or ``reasoning_effort``) plus the independent
``preserve_thinking`` kwarg when the template supports it.
"""
kwargs: dict = {}
# Always-on reasoning models hardcode <think> tags in their template
# and do not consume enable_thinking / reasoning_effort -- skip.
if self._supports_reasoning and not self._reasoning_always_on:
if self._reasoning_style == "reasoning_effort":
if reasoning_effort in ("low", "medium", "high"):
kwargs["reasoning_effort"] = reasoning_effort
elif enable_thinking is not None:
kwargs["reasoning_effort"] = "high" if enable_thinking else "low"
else:
if enable_thinking is not None:
kwargs["enable_thinking"] = enable_thinking
if self._supports_preserve_thinking and preserve_thinking is not None:
kwargs["preserve_thinking"] = preserve_thinking
return kwargs or None
@property
def supports_tools(self) -> bool:
return self._supports_tools
@property
def cache_type_kv(self) -> Optional[str]:
return self._cache_type_kv
@property
def speculative_type(self) -> Optional[str]:
return self._speculative_type
# ── Binary discovery ──────────────────────────────────────────
@staticmethod
def _find_llama_server_binary() -> Optional[str]:
"""
Locate the llama-server binary.
Search order:
1. LLAMA_SERVER_PATH environment variable (direct path to binary)
1b. UNSLOTH_LLAMA_CPP_PATH env var (custom llama.cpp install dir)
2. ~/.unsloth/llama.cpp/llama-server (make build, root dir)
3. ~/.unsloth/llama.cpp/build/bin/llama-server (cmake build, Linux)
4. ~/.unsloth/llama.cpp/build/bin/Release/llama-server.exe (cmake build, Windows)
5. ./llama.cpp/llama-server (legacy: make build, root dir)
6. ./llama.cpp/build/bin/llama-server (legacy: cmake in-tree build)
7. llama-server on PATH (system install)
8. ./bin/llama-server (legacy: extracted binary)
"""
import os
import sys
binary_name = "llama-server.exe" if sys.platform == "win32" else "llama-server"
# 1. Env var — direct path to binary
env_path = os.environ.get("LLAMA_SERVER_PATH")
if env_path and Path(env_path).is_file():
return env_path
# 1b. UNSLOTH_LLAMA_CPP_PATH — custom llama.cpp install directory
custom_llama_cpp = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
if custom_llama_cpp:
custom_dir = Path(custom_llama_cpp)
# Root dir (make builds)
root_bin = custom_dir / binary_name
if root_bin.is_file():
return str(root_bin)
# build/bin/ (cmake builds on Linux)
cmake_bin = custom_dir / "build" / "bin" / binary_name
if cmake_bin.is_file():
return str(cmake_bin)
# build/bin/Release/ (cmake builds on Windows)
if sys.platform == "win32":
win_bin = custom_dir / "build" / "bin" / "Release" / binary_name
if win_bin.is_file():
return str(win_bin)
# 2–4. ~/.unsloth/llama.cpp (primary — setup.sh / setup.ps1 build here)
unsloth_home = Path.home() / ".unsloth" / "llama.cpp"
# Root dir (make builds copy binaries here)
home_root = unsloth_home / binary_name
if home_root.is_file():
return str(home_root)
# build/bin/ (cmake builds on Linux)
home_linux = unsloth_home / "build" / "bin" / binary_name
if home_linux.is_file():
return str(home_linux)
# 3. Windows MSVC build has Release subdir
if sys.platform == "win32":
home_win = unsloth_home / "build" / "bin" / "Release" / binary_name
if home_win.is_file():
return str(home_win)
# 5–6. Legacy: in-tree build (older setup.sh / setup.ps1 versions)
project_root = Path(__file__).resolve().parents[4]
# Root dir (make builds)
root_path = project_root / "llama.cpp" / binary_name
if root_path.is_file():
return str(root_path)
# build/bin/ (cmake builds)
build_path = project_root / "llama.cpp" / "build" / "bin" / binary_name
if build_path.is_file():
return str(build_path)
if sys.platform == "win32":
win_path = (
project_root / "llama.cpp" / "build" / "bin" / "Release" / binary_name
)
if win_path.is_file():
return str(win_path)
# 7. System PATH
system_path = shutil.which("llama-server")
if system_path:
return system_path
# 8. Legacy: extracted to bin/
bin_path = project_root / "bin" / binary_name
if bin_path.is_file():
return str(bin_path)
return None
# ── GPU allocation ────────────────────────────────────────────
@staticmethod
def _get_gguf_size_bytes(model_path: str) -> int:
"""Get total GGUF size in bytes, including split shards."""
main = Path(model_path)
total = main.stat().st_size
# Check for split shards (e.g., model-00001-of-00003.gguf)
m = _SHARD_FULL_RE.match(main.name)
if m:
prefix, _, num_total = m.group(1), m.group(2), m.group(3)
sibling_pat = re.compile(
r"^"
+ re.escape(prefix)
+ r"-\d{5}-of-"
+ re.escape(num_total)
+ r"\.gguf$"
)
for sibling in main.parent.iterdir():
if sibling != main and sibling_pat.match(sibling.name):
total += sibling.stat().st_size
return total
@staticmethod
def _get_gpu_free_memory() -> list[tuple[int, int]]:
"""Query free memory per GPU.
Order:
1. ``nvidia-smi`` (NVIDIA CUDA hosts) -- respects
``CUDA_VISIBLE_DEVICES``.
2. ``torch.cuda.mem_get_info`` -- universal fallback that
works on AMD ROCm too because the HIP runtime
reuses the entire ``torch.cuda.*`` namespace. Covers the
AMD case for issue #5106 (nvidia-smi-only probe silently
returned [] on AMD hosts) and also rescues NVIDIA hosts
where ``nvidia-smi`` is missing from PATH.
Returns list of (gpu_index, free_mib) sorted by index. Empty
list if no supported GPU is reachable.
"""
import os
# ── NVIDIA via nvidia-smi ────────────────────────────────────
try:
result = subprocess.run(
[
"nvidia-smi",
"--query-gpu=index,memory.free",
"--format=csv,noheader,nounits",
],
capture_output = True,
text = True,
timeout = 10,
**_windows_hidden_subprocess_kwargs(),
)
if result.returncode == 0:
allowed: Optional[set[int]] = None
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
if cvd is not None:
try:
# `if x.strip()` filters trailing-comma masks like
# "0,1," which would otherwise raise ValueError on
# an empty token. An explicitly empty mask (CVD="")
# yields an empty `allowed` set so all GPUs are
# filtered out, matching the codebase convention.
allowed = set(
int(x.strip()) for x in cvd.split(",") if x.strip()
)
except ValueError:
pass
gpus: list[tuple[int, int]] = []
for line in result.stdout.strip().splitlines():
parts = line.split(",")
if len(parts) == 2:
idx = int(parts[0].strip())
free_mib = int(parts[1].strip())
if allowed is not None and idx not in allowed:
continue
gpus.append((idx, free_mib))
# Match the docstring's sort-by-id guarantee. nvidia-smi
# almost always returns sorted output, but driver order
# is not formally guaranteed.
gpus.sort(key = lambda g: g[0])
if gpus:
return gpus
except Exception as e:
logger.debug(f"nvidia-smi probe failed: {e}")
# ── Torch fallback (covers AMD ROCm and missing nvidia-smi) ──
try:
import torch
if not hasattr(torch, "cuda") or not torch.cuda.is_available():
return []
if not hasattr(torch.cuda, "mem_get_info"):
return []
# torch.cuda enumerates GPUs RELATIVE to the visibility mask.
# On NVIDIA builds the mask is CUDA_VISIBLE_DEVICES; on AMD
# ROCm builds it is HIP_VISIBLE_DEVICES (or ROCR_VISIBLE_DEVICES
# if HIP is unset). Downstream we feed these IDs back into the
# llama-server subprocess as CVD, so we must translate visible
# ordinals back to physical indices first; otherwise launching
# with ``CUDA_VISIBLE_DEVICES=2,3`` would get rewritten to
# ``CUDA_VISIBLE_DEVICES=0,1`` and target the wrong GPUs.
physical_ids: Optional[list[int]] = None
# Match the codebase convention in
# ``utils/hardware/hardware.py::_get_parent_visible_gpu_spec``:
# treat an explicitly empty mask (``HIP_VISIBLE_DEVICES=""``)
# as "set to no GPUs" rather than falling through to the next
# var. ``or`` would coerce empty string to falsy and silently
# promote the wrong source.
if getattr(torch.version, "hip", None) is not None:
hip_v = os.environ.get("HIP_VISIBLE_DEVICES")
rocr_v = os.environ.get("ROCR_VISIBLE_DEVICES")
cvd = (
hip_v
if hip_v is not None
else rocr_v
if rocr_v is not None
else os.environ.get("CUDA_VISIBLE_DEVICES")
)
else:
cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
if cvd is not None:
try:
# Empty mask (CVD="") yields an empty list so the
# below loop produces no GPUs, consistent with the
# nvidia-smi path and utils/hardware/hardware.py.
physical_ids = [int(x.strip()) for x in cvd.split(",") if x.strip()]
except ValueError:
physical_ids = None
gpus = []
for ordinal in range(torch.cuda.device_count()):
free_bytes, _total_bytes = torch.cuda.mem_get_info(ordinal)
idx = (
physical_ids[ordinal]
if physical_ids is not None and ordinal < len(physical_ids)
else ordinal
)
gpus.append((idx, free_bytes // (1024 * 1024)))
# Match the nvidia-smi path's docstring guarantee of sorted-by-id.
return sorted(gpus, key = lambda g: g[0])
except Exception as e:
logger.debug(f"torch GPU probe failed: {e}")
return []
@staticmethod
def _select_gpus(
model_size_bytes: int,
gpus: list[tuple[int, int]],
) -> tuple[Optional[list[int]], bool]:
"""Pick GPU(s) for a model based on estimated VRAM and free memory.
``model_size_bytes`` should include both model weights and estimated
KV cache. The 90% threshold provides headroom for compute buffers,
CUDA context, and other runtime overhead.
Returns (gpu_indices, use_fit):
- ([1], False) model fits on 1 GPU at 90% of free
- ([1, 2], False) model needs 2 GPUs
- (None, True) model too large, let --fit handle it
"""
if not gpus:
return None, True
model_size_mib = model_size_bytes / (1024 * 1024)
# Sort GPUs by free memory descending
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
# Try fitting on 1 GPU (90% of free memory threshold)
if ranked[0][1] * 0.90 >= model_size_mib:
return [ranked[0][0]], False
# Try fitting on N GPUs (accumulate free memory from most-free)
cumulative = 0
selected = []
for idx, free_mib in ranked:
selected.append(idx)
cumulative += free_mib * 0.90
if cumulative >= model_size_mib:
return sorted(selected), False
# Model is too large even for all GPUs, let --fit handle it
logger.debug(
"Model does not fit in available GPU memory, falling back to --fit",
model_size_mib = round(model_size_mib, 2),
ranked_gpus = ranked,
)
return None, True
# ── KV cache VRAM estimation ─────────────────────────────────────
def _can_estimate_kv(self) -> bool:
"""True if we have enough GGUF metadata to estimate KV cache size."""
if self._n_layers is None:
return False
# MLA: kv_lora_rank is sufficient (K-only cache)
if self._kv_lora_rank is not None:
return True
# New-style: need both explicit key AND value dimensions
if self._kv_key_length is not None and self._kv_value_length is not None:
return True
# Legacy: need embedding_length + head count
return self._embedding_length is not None and (
self._n_kv_heads is not None or self._n_heads is not None
)
def _estimate_kv_cache_bytes(
self, n_ctx: int, cache_type_kv: Optional[str] = None
) -> int:
"""Estimate KV cache VRAM for a given context length.
Uses 5-path architecture-aware estimation:
1. MLA -- compressed KV latent + RoPE, K-only (no separate V)
2. Hybrid -- only attention layers need KV (Mamba layers don't)
3. SWA -- sliding-window layers cache min(ctx, window) tokens
4. GQA -- standard full KV with explicit key/value dimensions
5. Legacy -- fallback using embed // n_heads
Returns 0 if metadata is insufficient for estimation.
"""
if not self._can_estimate_kv() or n_ctx <= 0:
return 0
n_layers = self._n_layers # type: ignore[assignment]
n_kv = self._n_kv_heads or self._n_heads or 1 # type: ignore[assignment]
# Bytes per element depends on KV cache quantization
bpe = {
"f32": 4.0,
"f16": 2.0,
"bf16": 2.0,
"q8_0": 34 / 32,
"q5_1": 0.75,
"q5_0": 0.6875,
"q4_1": 0.625,
"q4_0": 0.5625,
"iq4_nl": 0.5625,
}.get(cache_type_kv or "f16", 2.0)
# Path 1: MLA (DeepSeek-V2/V3, GLM-4.7, GLM-5, Kimi-K2.5)
# MLA stores one compressed KV latent per token/layer (shared across heads).
# V is reconstructed from the latent on the fly -- no separate V cache.
# key_length = kv_lora_rank + rope_dim (the full compressed representation).
# MLA GGUFs set head_count_kv=1; default to 1 if absent to avoid
# falling back to n_heads (e.g., 128 for DeepSeek-V3) which would 128x.
if self._kv_lora_rank is not None:
n_kv_mla = self._n_kv_heads or 1
rope_dim = self._key_length_mla or 64
key_len = self._kv_key_length or (self._kv_lora_rank + rope_dim)
return int(n_layers * n_ctx * n_kv_mla * key_len * bpe)
key_len = self._kv_key_length
val_len = self._kv_value_length
# Path 2: Hybrid Mamba/Attention (Qwen3.5-27B, Qwen3.5-35B-A3B)
# Only 1 in N layers is attention; the rest are Mamba (no KV cache).
if (
self._ssm_inner_size is not None
and self._full_attention_interval is not None
):
fai = self._full_attention_interval
n_attn = -(-n_layers // fai) if fai > 0 else n_layers # ceiling division
if key_len is not None and val_len is not None:
return int(n_attn * n_ctx * n_kv * (key_len + val_len) * bpe)
head_dim = self._embedding_length // self._n_heads if self._n_heads else 128 # type: ignore[operator]
return int(n_attn * n_ctx * n_kv * 2 * head_dim * bpe)
# Path 3: Sliding Window (Gemma-3, gpt-oss)
# SWA layers only cache min(ctx, window) tokens; global layers cache full ctx.
# Most SWA architectures use few global layers (e.g., Gemma-3 uses 1 in 6).
# Without an explicit field, we conservatively assume 1/4 of layers are global
# which is still far more accurate than the legacy formula (which ignores SWA).
if (
self._sliding_window is not None
and self._sliding_window > 0
and key_len is not None
and val_len is not None
):
swa = self._sliding_window
n_global = max(1, n_layers // 4)
n_swa = n_layers - n_global
kv_per_token = n_kv * (key_len + val_len) * bpe
return int(
n_global * n_ctx * kv_per_token + n_swa * min(n_ctx, swa) * kv_per_token
)
# Path 4: Standard GQA with explicit key/value dimensions
if key_len is not None and val_len is not None:
return int(n_layers * n_ctx * n_kv * (key_len + val_len) * bpe)
# Path 5: Legacy fallback (old GGUFs without explicit dimensions)
head_dim = self._embedding_length // self._n_heads if self._n_heads else 128 # type: ignore[operator]
return int(2 * n_kv * head_dim * n_layers * n_ctx * bpe)
def _fit_context_to_vram(
self,
requested_ctx: int,
available_mib: int,
model_size_bytes: int,
cache_type_kv: Optional[str] = None,
min_ctx: int = 4096,
) -> int:
"""Return the largest context length that fits in GPU VRAM.
Uses 90% of available VRAM as the budget (matching _select_gpus
threshold -- 10% reserved for compute buffers, CUDA context,
scratch space, flash-attn workspace, etc.).
If the model weights alone don't fit, returns min_ctx unchanged.
"""
if not self._can_estimate_kv():
logger.debug(
"Skipping context fit because KV cache metadata is unavailable",
requested_ctx = requested_ctx,
available_mib = available_mib,
)
return requested_ctx
budget_bytes = available_mib * 1024 * 1024 * 0.90
model_footprint = model_size_bytes
# Check if requested context already fits
kv = self._estimate_kv_cache_bytes(requested_ctx, cache_type_kv)
if model_footprint + kv <= budget_bytes:
return requested_ctx
# Model weights alone exceed budget -- can't help by reducing ctx.
# Return requested_ctx unchanged; --fit will handle VRAM management.
if model_footprint >= budget_bytes:
logger.debug(
"Model footprint exceeds GPU budget before KV cache",
requested_ctx = requested_ctx,
available_mib = available_mib,
model_size_gb = round(model_footprint / (1024**3), 2),
)
return requested_ctx
# Binary search for max context that fits
remaining = budget_bytes - model_footprint
effective_min = min(min_ctx, requested_ctx)
lo, hi = effective_min, requested_ctx
best = effective_min
while lo <= hi:
mid = (lo + hi) // 2
kv = self._estimate_kv_cache_bytes(mid, cache_type_kv)
if kv <= remaining:
best = mid
lo = mid + 1
else:
hi = mid - 1
# Round down to nearest 256 for alignment, but never exceed requested_ctx
best = (best // 256) * 256
best = max(effective_min, best)
best = min(best, requested_ctx)
return best
# ── Variant fallback ────────────────────────────────────────────
@staticmethod
def _find_smallest_fitting_variant(
hf_repo: str,
free_bytes: int,
hf_token: Optional[str] = None,
) -> Optional[tuple[str, int]]:
"""Find the smallest GGUF variant (including all shards) that fits.
Groups split shards by variant prefix and sums their sizes.
For example, UD-Q4_K_XL with 9 shards of 50 GB each = 450 GB total.
Returns (first_shard_filename, total_size_bytes) or None if nothing fits.
"""
try:
from huggingface_hub import get_paths_info, list_repo_files
files = list_repo_files(hf_repo, token = hf_token)
gguf_files = [
f for f in files if f.endswith(".gguf") and "mmproj" not in f.lower()
]
if not gguf_files:
return None
# Get sizes for all GGUF files
path_infos = list(get_paths_info(hf_repo, gguf_files, token = hf_token))
size_map = {p.path: (p.size or 0) for p in path_infos}
# Group files by variant: shards share a prefix before -NNNNN-of-NNNNN
variants: dict[str, list[str]] = {}
for f in gguf_files:
m = _SHARD_RE.match(f)
key = m.group(1) if m else f
variants.setdefault(key, []).append(f)
# Sum shard sizes per variant, track the first shard (for download)
variant_sizes: list[tuple[str, int, list[str]]] = []
for key, shard_files in variants.items():
total = sum(size_map.get(f, 0) for f in shard_files)
first = sorted(shard_files)[0]
variant_sizes.append((first, total, shard_files))
# Sort by total size ascending and pick the smallest that fits
variant_sizes.sort(key = lambda x: x[1])
for first_file, total_size, _ in variant_sizes:
if total_size > 0 and total_size <= free_bytes:
return first_file, total_size
return None
except Exception:
return None
# ── Port allocation ───────────────────────────────────────────
@staticmethod
def _find_free_port() -> int:
"""Find an available TCP port."""
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(("127.0.0.1", 0))
return s.getsockname()[1]
# ── Stdout drain (prevents pipe deadlock on Windows) ─────────
def _drain_stdout(self):
"""
Read lines from the subprocess stdout in a background thread.
This prevents a pipe-buffer deadlock on Windows where the default
pipe buffer is only ~4 KB. Without draining, llama-server blocks
on writes and never becomes healthy.
"""
try:
for line in self._process.stdout:
line = line.rstrip()
if line:
self._stdout_lines.append(line)
logger.debug(f"[llama-server] {line}")
except (ValueError, OSError):
# Pipe closed — process is terminating
pass
# GGUF KV type sizes for fast skipping
_GGUF_TYPE_SIZE = {
0: 1,
1: 1,
2: 2,
3: 2,
4: 4,
5: 4,
6: 4,
7: 1,
10: 8,
11: 8,
12: 8,
}
@staticmethod
def _gguf_skip_value(f, vtype: int) -> None:
"""Skip a GGUF KV value without reading it."""
sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(vtype)
if sz is not None:
f.seek(sz, 1)
elif vtype == 8: # STRING
slen = struct.unpack("<Q", f.read(8))[0]
f.seek(slen, 1)
elif vtype == 9: # ARRAY
atype = struct.unpack("<I", f.read(4))[0]
alen = struct.unpack("<Q", f.read(8))[0]
elem_sz = LlamaCppBackend._GGUF_TYPE_SIZE.get(atype)
if elem_sz is not None:
f.seek(elem_sz * alen, 1)
elif atype == 8:
for _ in range(alen):
slen = struct.unpack("<Q", f.read(8))[0]
f.seek(slen, 1)
else:
for _ in range(alen):
LlamaCppBackend._gguf_skip_value(f, atype)
def _read_gguf_metadata(self, gguf_path: str) -> None:
"""Read context_length, architecture params, and chat_template from a GGUF header.
Parses only the KV pairs we need (~30ms even for multi-GB files).
For split GGUFs, metadata is always in shard 1.
"""
# Reset metadata from any previously loaded model so stale flags
# (eg _supports_reasoning) do not carry over when switching models.
self._context_length = None
self._chat_template = None
self._supports_reasoning = False
self._reasoning_always_on = False
self._reasoning_style = "enable_thinking"
self._reasoning_default = True
self._supports_preserve_thinking = False
self._supports_tools = False
self._n_layers = None
self._n_kv_heads = None
self._n_heads = None
self._embedding_length = None
self._kv_key_length = None
self._kv_value_length = None
self._sliding_window = None
self._full_attention_interval = None
self._kv_lora_rank = None
self._key_length_mla = None
self._ssm_inner_size = None
self._ssm_state_size = None
try:
WANTED = {"general.architecture", "tokenizer.chat_template"}
# Additional arch-specific keys are added dynamically once
# we know the architecture name.
arch_keys: dict[str, str] = {} # gguf_key -> attribute name
arch = None
with open(gguf_path, "rb") as f:
magic = struct.unpack("<I", f.read(4))[0]
if magic != 0x46554747: # b"GGUF" as little-endian u32
return
_version = struct.unpack("<I", f.read(4))[0]
_tensor_count, kv_count = struct.unpack("<QQ", f.read(16))
for _ in range(kv_count):
key_len = struct.unpack("<Q", f.read(8))[0]
key = f.read(key_len).decode("utf-8")
vtype = struct.unpack("<I", f.read(4))[0]
if key in WANTED or key in arch_keys:
# Read this value
if vtype == 8: # STRING
slen = struct.unpack("<Q", f.read(8))[0]
val_s = f.read(slen).decode("utf-8")
if key == "general.architecture":
arch = val_s
# Register arch-specific keys to look for
arch_keys = {
f"{arch}.context_length": "context_length",
f"{arch}.block_count": "n_layers",
f"{arch}.attention.head_count_kv": "n_kv_heads",
f"{arch}.attention.head_count": "n_heads",
f"{arch}.embedding_length": "embedding_length",
# Architecture-aware KV cache fields
f"{arch}.attention.key_length": "kv_key_length",
f"{arch}.attention.value_length": "kv_value_length",
f"{arch}.attention.sliding_window": "sliding_window",
f"{arch}.full_attention_interval": "full_attention_interval",
f"{arch}.attention.kv_lora_rank": "kv_lora_rank",
f"{arch}.attention.key_length_mla": "key_length_mla",
f"{arch}.ssm.inner_size": "ssm_inner_size",
f"{arch}.ssm.state_size": "ssm_state_size",
}
elif key == "tokenizer.chat_template":
self._chat_template = val_s
elif vtype in (4, 10): # UINT32 or UINT64
val_i = (
struct.unpack("<I", f.read(4))[0]
if vtype == 4
else struct.unpack("<Q", f.read(8))[0]
)
attr = arch_keys.get(key)
if attr:
setattr(self, f"_{attr}", val_i)
else:
self._gguf_skip_value(f, vtype)
else:
self._gguf_skip_value(f, vtype)
if self._context_length:
logger.info(f"GGUF metadata: context_length={self._context_length}")
if self._chat_template:
logger.info(
f"GGUF metadata: chat_template={len(self._chat_template)} chars"
)
# Detect thinking/reasoning support from chat template
flags = detect_reasoning_flags(
self._chat_template,
self._model_identifier,
log_source = "GGUF metadata",
)
self._supports_reasoning = flags["supports_reasoning"]
self._reasoning_style = flags["reasoning_style"]
self._reasoning_always_on = flags["reasoning_always_on"]
self._supports_preserve_thinking = flags["supports_preserve_thinking"]
self._supports_tools = flags["supports_tools"]
except Exception as e:
logger.warning(f"Failed to read GGUF metadata: {e}")
# ── HF download (no lock held) ───────────────────────────────
def _download_gguf(
self,
*,
hf_repo: str,
hf_variant: Optional[str] = None,
hf_token: Optional[str] = None,
) -> str:
"""Download GGUF file(s) from HuggingFace. Returns local path.
Runs WITHOUT self._lock so that unload_model() can set
_cancel_event at any time. Checks _cancel_event between
each shard download.
"""
try:
from huggingface_hub import hf_hub_download
except ImportError:
raise RuntimeError(
"huggingface_hub is required for HF model loading. "
"Install it with: pip install huggingface_hub"
)
# Determine the filename from the variant
gguf_filename = None
gguf_extra_shards: list[str] = []
if hf_variant:
try:
from huggingface_hub import list_repo_files
files = list_repo_files(hf_repo, token = hf_token)
variant_lower = hf_variant.lower()
boundary = re.compile(
r"(?<![a-zA-Z0-9])" + re.escape(variant_lower) + r"(?![a-zA-Z0-9])"
)
gguf_files = sorted(
f
for f in files
if f.endswith(".gguf") and boundary.search(f.lower())
)
if gguf_files:
gguf_filename = gguf_files[0]
m = _SHARD_FULL_RE.match(gguf_filename)
if m:
prefix = m.group(1)
total = m.group(3)
sibling_pat = re.compile(
r"^"
+ re.escape(prefix)
+ r"-\d{5}-of-"
+ re.escape(total)
+ r"\.gguf$"
)
gguf_extra_shards = [
f for f in gguf_files[1:] if sibling_pat.match(f)
]
except Exception as e:
logger.warning(f"Could not list repo files: {e}")
if not gguf_filename:
repo_name = hf_repo.split("/")[-1].replace("-GGUF", "")
gguf_filename = f"{repo_name}-{hf_variant}.gguf"
# Check disk space and fall back to a smaller variant if needed
all_gguf_files = [gguf_filename] + gguf_extra_shards
try:
import os
from huggingface_hub import get_paths_info, try_to_load_from_cache
path_infos = list(get_paths_info(hf_repo, all_gguf_files, token = hf_token))
total_bytes = sum((p.size or 0) for p in path_infos)
# Subtract bytes already present in the HF cache so we only
# preflight against what we actually have to download. Without
# this, re-loading a cached large model (e.g. MiniMax-M2.7-GGUF
# at 131 GB) fails cold whenever free disk is below the full
# weight footprint, even though nothing needs downloading.
already_cached_bytes = 0
for p in path_infos:
if not p.size:
continue
try:
cached_path = try_to_load_from_cache(hf_repo, p.path)
except Exception:
cached_path = None
if isinstance(cached_path, str) and os.path.exists(cached_path):
try:
on_disk = os.path.getsize(cached_path)
except OSError:
on_disk = 0
# Count as satisfied only when the full blob is present.
if on_disk >= p.size:
already_cached_bytes += p.size
total_download_bytes = max(0, total_bytes - already_cached_bytes)
if total_download_bytes > 0:
cache_dir = os.environ.get(
"HF_HUB_CACHE",
str(Path.home() / ".cache" / "huggingface" / "hub"),
)
Path(cache_dir).mkdir(parents = True, exist_ok = True)
free_bytes = shutil.disk_usage(cache_dir).free
total_gb = total_download_bytes / (1024**3)
free_gb = free_bytes / (1024**3)
cached_gb = already_cached_bytes / (1024**3)
logger.info(
f"GGUF download: {total_gb:.1f} GB needed "
f"({cached_gb:.1f} GB already cached), "
f"{free_gb:.1f} GB free on disk"
)
if total_download_bytes > free_bytes:
smaller = self._find_smallest_fitting_variant(
hf_repo,
free_bytes,
hf_token,
)
if smaller:
fallback_file, fallback_size = smaller
logger.info(
f"Selected variant too large ({total_gb:.1f} GB), "
f"falling back to {fallback_file} ({fallback_size / (1024**3):.1f} GB)"
)
gguf_filename = fallback_file
_m = _SHARD_RE.match(gguf_filename)
_prefix = _m.group(1) if _m else None
if _prefix:
gguf_extra_shards = sorted(
f
for f in all_gguf_files
if f.startswith(_prefix)
and f != gguf_filename
and "mmproj" not in f.lower()
)
else:
gguf_extra_shards = []
else:
raise RuntimeError(
f"Not enough disk space to download any variant. "
f"Only {free_gb:.1f} GB free in {cache_dir}"
)
except RuntimeError:
raise
except Exception as e:
logger.warning(f"Could not check disk space: {e}")
gguf_label = f"{hf_repo}/{gguf_filename}" + (
f" (+{len(gguf_extra_shards)} shards)" if gguf_extra_shards else ""
)
logger.info(f"Resolving GGUF: {gguf_label}")
try:
if self._cancel_event.is_set():
raise RuntimeError("Cancelled")
dl_start = time.monotonic()
local_path = hf_hub_download(
repo_id = hf_repo,
filename = gguf_filename,
token = hf_token,
)
for shard in gguf_extra_shards:
if self._cancel_event.is_set():
raise RuntimeError("Cancelled")
logger.info(f"Resolving GGUF shard: {shard}")
hf_hub_download(
repo_id = hf_repo,
filename = shard,
token = hf_token,
)
except RuntimeError as e:
if "Cancelled" in str(e):
raise
raise RuntimeError(
f"Failed to download GGUF file '{gguf_filename}' from {hf_repo}: {e}"
)
except Exception as e:
raise RuntimeError(
f"Failed to download GGUF file '{gguf_filename}' from {hf_repo}: {e}"
)
dl_elapsed = time.monotonic() - dl_start
if dl_elapsed < 2.0:
logger.info(f"GGUF resolved from cache: {local_path}")
else:
logger.info(f"GGUF downloaded in {dl_elapsed:.1f}s: {local_path}")
return local_path
def _download_mmproj(
self,
*,
hf_repo: str,
hf_token: Optional[str] = None,
) -> Optional[str]:
"""Download the mmproj (vision projection) file from a GGUF repo.
Prefers mmproj-F16.gguf, falls back to any mmproj*.gguf file.
Returns the local path, or None if no mmproj file exists.
"""
try:
from huggingface_hub import hf_hub_download, list_repo_files
files = list_repo_files(hf_repo, token = hf_token)
mmproj_files = sorted(
f for f in files if f.endswith(".gguf") and "mmproj" in f.lower()
)
if not mmproj_files:
return None
# Prefer F16 variant
target = None
for f in mmproj_files:
if f.lower().endswith("-f16.gguf"):
target = f
break
if target is None:
target = mmproj_files[0]
logger.info(f"Downloading mmproj: {hf_repo}/{target}")
local_path = hf_hub_download(
repo_id = hf_repo,
filename = target,
token = hf_token,
)
return local_path
except Exception as e:
logger.warning(f"Could not download mmproj: {e}")
return None
# ── Lifecycle ─────────────────────────────────────────────────
def load_model(
self,
*,
# Local mode: pass a path to a .gguf file
gguf_path: Optional[str] = None,
# Vision projection (mmproj) for local vision models
mmproj_path: Optional[str] = None,
# HF mode: let llama-server download via -hf "repo:quant"
hf_repo: Optional[str] = None,
hf_variant: Optional[str] = None,
hf_token: Optional[str] = None,
# Common
model_identifier: str,
is_vision: bool = False,
n_ctx: int = 4096,
chat_template_override: Optional[str] = None,
cache_type_kv: Optional[str] = None,
speculative_type: Optional[str] = None,
n_threads: Optional[int] = None,
n_gpu_layers: Optional[int] = None, # Accepted for caller compat, unused
n_parallel: int = 1,
) -> bool:
"""
Start llama-server with a GGUF model.
Two modes:
- Local: ``gguf_path="/path/to/model.gguf"`` → uses ``-m``
- HF: ``hf_repo="unsloth/gemma-3-4b-it-GGUF", hf_variant="Q4_K_M"`` → uses ``-hf``
In HF mode, llama-server handles downloading, caching, and
auto-loading mmproj files for vision models.
Returns True if server started and health check passed.
"""
self._cancel_event.clear()
# ── Phase 1: kill old process (under lock, fast) ──────────
with self._lock:
self._kill_process()
binary = self._find_llama_server_binary()
if not binary:
raise RuntimeError(
"llama-server binary not found. "
"Run setup.sh to build it, install llama.cpp, "
"or set LLAMA_SERVER_PATH environment variable."
)
# ── Phase 2: download (NO lock held, so cancel can proceed) ──
if hf_repo:
model_path = self._download_gguf(
hf_repo = hf_repo,
hf_variant = hf_variant,
hf_token = hf_token,
)
# Auto-download mmproj for vision models
if is_vision and not mmproj_path:
mmproj_path = self._download_mmproj(
hf_repo = hf_repo,
hf_token = hf_token,
)
elif gguf_path:
if not Path(gguf_path).is_file():
raise FileNotFoundError(f"GGUF file not found: {gguf_path}")
model_path = gguf_path
else:
raise ValueError("Either gguf_path or hf_repo must be provided")
# Set identifier early so _read_gguf_metadata can use it for DeepSeek detection
self._model_identifier = model_identifier
# Read GGUF metadata (context_length, chat_template) -- fast, header only
self._read_gguf_metadata(model_path)
# Check cancel after download
if self._cancel_event.is_set():
logger.info("Load cancelled after download phase")
return False
# ── Phase 3: start llama-server (under lock) ──────────────
with self._lock:
# Re-check cancel inside lock
if self._cancel_event.is_set():
logger.info("Load cancelled before server start")
return False
self._port = self._find_free_port()
# Select GPU(s) based on model size + estimated KV cache.
# Seed safe defaults before GPU probing so the except path
# still has valid state to publish.
effective_ctx = n_ctx if n_ctx > 0 else (self._context_length or 0)
max_available_ctx = self._context_length or effective_ctx
try:
model_size = self._get_gguf_size_bytes(model_path)
gpus = self._get_gpu_free_memory()
# Resolve effective context: 0 means let llama-server use the
# model's native length. Only expand to a known native length
# if metadata is available; otherwise preserve 0 as a sentinel.
if n_ctx > 0:
effective_ctx = n_ctx
elif self._context_length is not None:
effective_ctx = self._context_length
else:
effective_ctx = 0
original_ctx = effective_ctx
# Default UI ceiling to the model's native context length.
# GPU/VRAM-fit logic below may shrink this if hardware is limited.
max_available_ctx = self._context_length or effective_ctx
# Auto-cap context to fit in GPU VRAM and select GPUs.
#
# Two policies depending on whether the user set n_ctx:
#
# Explicit n_ctx (user chose a context length):
# Honor it. Try the full requested context with _select_gpus
# (which uses as many GPUs as needed). Only cap if it doesn't
# fit on any GPU combination.
#
# Auto n_ctx=0 (model's native context):
# Prefer fewer GPUs with reduced context over more GPUs,
# since multi-GPU is slower and the user didn't ask for a
# specific context length.
gpu_indices, use_fit = None, True
explicit_ctx = n_ctx > 0
if gpus and self._can_estimate_kv() and effective_ctx > 0:
# Compute the largest hardware-aware cap from the model's
# native context across all usable GPU subsets (for UI
# bounds), independent of the currently requested context.
native_ctx_for_cap = self._context_length or effective_ctx
if native_ctx_for_cap > 0:
ranked_for_cap = sorted(gpus, key = lambda g: g[1], reverse = True)
best_cap = 0
for n_gpus in range(1, len(ranked_for_cap) + 1):
subset = ranked_for_cap[:n_gpus]
pool_mib = sum(free for _, free in subset)
capped = self._fit_context_to_vram(
native_ctx_for_cap,
pool_mib,
model_size,
cache_type_kv,
)
kv = self._estimate_kv_cache_bytes(capped, cache_type_kv)
total_mib = (model_size + kv) / (1024 * 1024)
if total_mib <= pool_mib * 0.90:
best_cap = max(best_cap, capped)
if best_cap > 0:
max_available_ctx = best_cap
else:
# Weights exceed 90% of every GPU subset's free
# memory, so there is no fitting context. Anchor
# the UI's "safe zone" threshold at 4096 (the
# spec's default when the model cannot fit) so
# the ctx slider shows the "might be slower"
# warning as soon as the user drags above the
# fallback default instead of never.
max_available_ctx = min(4096, native_ctx_for_cap)
if explicit_ctx:
# Honor the user's requested context verbatim. If it
# fits, pin GPUs and skip --fit; if it doesn't, ship
# -c <user_ctx> --fit on and let llama-server flex
# -ngl (CPU layer offload). The UI is expected to
# have surfaced the "might be slower" warning before
# the user submitted a ctx above the fit ceiling.
requested_total = model_size + self._estimate_kv_cache_bytes(
effective_ctx, cache_type_kv
)
gpu_indices, use_fit = self._select_gpus(requested_total, gpus)
# No silent shrink: effective_ctx stays == n_ctx.
else:
# Auto context: prefer fewer GPUs, cap context to fit.
ranked = sorted(gpus, key = lambda g: g[1], reverse = True)
for n_gpus in range(1, len(ranked) + 1):
subset = ranked[:n_gpus]
pool_mib = sum(free for _, free in subset)
capped = self._fit_context_to_vram(
effective_ctx,
pool_mib,
model_size,
cache_type_kv,
)
kv = self._estimate_kv_cache_bytes(capped, cache_type_kv)
total_mib = (model_size + kv) / (1024 * 1024)
if total_mib <= pool_mib * 0.90:
effective_ctx = capped
gpu_indices = sorted(idx for idx, _ in subset)
use_fit = False
break
else:
# No subset can host the weights (weights alone
# exceed 90% of every pool). Per spec, default
# the UI-visible context to 4096 and let
# --fit on flex -ngl so llama-server offloads
# layers to CPU RAM.
effective_ctx = min(4096, effective_ctx)
elif gpus:
# Can't estimate KV -- fall back to file-size-only check.
# Without KV estimation we cannot prove a hardware cap, so
# keep the ceiling at the native context (already the default).
logger.debug(
"Falling back to file-size-only GPU selection",
model_size_gb = round(model_size / (1024**3), 2),
)
gpu_indices, use_fit = self._select_gpus(model_size, gpus)
if use_fit and not explicit_ctx:
# Weights don't fit on any subset. Default the UI to
# 4096 so the slider doesn't land on an unusable native
# context. --fit on will flex -ngl at runtime.
effective_ctx = (
min(4096, effective_ctx) if effective_ctx > 0 else 4096
)
if effective_ctx < original_ctx:
kv_est = self._estimate_kv_cache_bytes(effective_ctx, cache_type_kv)
logger.info(
f"Context auto-reduced: {original_ctx} -> {effective_ctx} "
f"(model: {model_size / (1024**3):.1f} GB, "
f"est. KV cache: {kv_est / (1024**3):.1f} GB)"
)
kv_cache_bytes = self._estimate_kv_cache_bytes(
effective_ctx, cache_type_kv
)
logger.info(
f"GGUF size: {model_size / (1024**3):.1f} GB, "
f"est. KV cache: {kv_cache_bytes / (1024**3):.1f} GB, "
f"context: {effective_ctx}, "
f"GPUs free: {gpus}, selected: {gpu_indices}, fit: {use_fit}"
)
except Exception as e:
logger.warning(f"GPU selection failed ({e}), using --fit on")
gpu_indices, use_fit = None, True
effective_ctx = n_ctx # fall back to original
cmd = [
binary,
"-m",
model_path,
"--port",
str(self._port),
"-c",
str(effective_ctx) if effective_ctx > 0 else "0",
"--parallel",
str(n_parallel),
"--flash-attn",
"on", # Force flash attention for speed
# Error out at n_ctx instead of silently rotating the KV cache; frontend catches it and points the user at "Context Length".
"--no-context-shift",
]
if use_fit:
cmd.extend(["--fit", "on"])
elif gpu_indices is not None:
# Model fits on selected GPU(s) -- offload all layers
cmd.extend(["-ngl", "-1"])
# -1 = llama.cpp auto-detect (physical cores). Pass explicitly so we
# do not inherit llama-server's internal default, which has historically
# varied (hardware concurrency incl. hyperthreads on some builds).
cmd.extend(["--threads", str(n_threads if n_threads is not None else -1)])
# Always enable Jinja chat template rendering for proper template support
cmd.extend(["--jinja"])
# KV cache data type
_valid_cache_types = {
"f16",
"bf16",
"q8_0",
"q4_0",
"q4_1",
"q5_0",
"q5_1",
"iq4_nl",
"f32",
}
if cache_type_kv and cache_type_kv in _valid_cache_types:
cmd.extend(
["--cache-type-k", cache_type_kv, "--cache-type-v", cache_type_kv]
)
self._cache_type_kv = cache_type_kv
logger.info(f"KV cache type: {cache_type_kv}")
else:
self._cache_type_kv = None
# Speculative decoding (n-gram self-speculation, zero VRAM cost)
# ngram-mod: ~16 MB shared hash pool, constant memory/complexity,
# variable draft lengths. Helps most when the model repeats
# existing text (code refactoring, summarization, reasoning).
# For general chat with low repetition, overhead is ~5 ms.
#
# Benchmarks from upstream llama.cpp speculative-decoding PRs:
# Scenario | Without | With | Speedup
# gpt-oss-120b code refactor | 181 t/s | 446 t/s | 2.5x
# Qwen3-235B offloaded | 12 t/s | 21 t/s | 1.8x
# gpt-oss-120b repeat (92% accept)| 181 t/s | 814 t/s | 4.5x
#
# Params from llama.cpp docs (docs/speculative.md):
# --spec-ngram-size-n 24 (small n not recommended)
# --draft-min 48 --draft-max 64 (MoEs need long drafts;
# dense models can reduce these)
# ref: https://github.com/ggml-org/llama.cpp/blob/master/docs/speculative.md
# ref: https://github.com/ggml-org/llama.cpp/pull/19164
# ref: https://github.com/ggml-org/llama.cpp/pull/18471
# ``"default"`` -> let llama-server pick a sensible spec
# config via ``--spec-default``. Explicit type names are
# passed through with the manual draft tuning we've shipped
# historically so power users keep their overrides.
_valid_spec_types = {"ngram-simple", "ngram-mod"}
normalized_spec = (
speculative_type.lower().strip() if speculative_type else None
)
if normalized_spec and normalized_spec != "off" and not is_vision:
if normalized_spec == "default":
cmd.append("--spec-default")
self._speculative_type = "default"
elif normalized_spec in _valid_spec_types:
cmd.extend(["--spec-type", normalized_spec])
if normalized_spec == "ngram-mod":
cmd.extend(
[
"--spec-ngram-size-n",
"24",
"--draft-min",
"48",
"--draft-max",
"64",
]
)
self._speculative_type = normalized_spec
else:
self._speculative_type = None
else:
self._speculative_type = None
# Apply custom chat template override if provided
if chat_template_override:
import tempfile
self._chat_template_file = tempfile.NamedTemporaryFile(
mode = "w",
suffix = ".jinja",
delete = False,
prefix = "unsloth_chat_template_",
)
self._chat_template_file.write(chat_template_override)
self._chat_template_file.close()
cmd.extend(["--chat-template-file", self._chat_template_file.name])
logger.info(
f"Using custom chat template file: {self._chat_template_file.name}"
)
# For reasoning models, set default thinking mode.
# Qwen3.5/3.6 models below 9B (0.8B, 2B, 4B) disable thinking by default.
# Only 9B and larger enable thinking.
# Always-on templates ignore the kwarg entirely, so skip.
if self._supports_reasoning and not self._reasoning_always_on:
thinking_default = True
mid = (model_identifier or "").lower()
if "qwen3.5" in mid or "qwen3.6" in mid:
size_val = _extract_model_size_b(mid)
if size_val is not None and size_val < 9:
thinking_default = False
self._reasoning_default = thinking_default
reasoning_kw = self._reasoning_kwargs(thinking_default)
cmd.extend(
[
"--chat-template-kwargs",
json.dumps(reasoning_kw),
]
)
logger.info(f"Reasoning model: {reasoning_kw} by default")
if mmproj_path:
if not Path(mmproj_path).is_file():
logger.warning(f"mmproj file not found: {mmproj_path}")
else:
cmd.extend(["--mmproj", mmproj_path])
logger.info(f"Using mmproj for vision: {mmproj_path}")
# Option C: add --api-key for direct client access when enabled
import os as _os
import secrets as _secrets
if _os.getenv("UNSLOTH_DIRECT_STREAM", "0") == "1":
self._api_key = _secrets.token_urlsafe(32)
cmd.extend(["--api-key", self._api_key])
logger.info("llama-server started with --api-key for direct streaming")
else:
self._api_key = None
_log_cmd = list(cmd)
if "--api-key" in _log_cmd:
_ki = _log_cmd.index("--api-key") + 1
if _ki < len(_log_cmd):
_log_cmd[_ki] = "<redacted>"
logger.info(f"Starting llama-server: {' '.join(_log_cmd)}")
# Set library paths so llama-server can find its shared libs and CUDA DLLs
import os
import sys
env = os.environ.copy()
binary_dir = str(Path(binary).parent)
if sys.platform == "win32":
# On Windows, CUDA DLLs (cublas64_12.dll, cudart64_12.dll, etc.)
# must be on PATH. Add CUDA_PATH\bin if available.
path_dirs = [binary_dir]
cuda_path = os.environ.get("CUDA_PATH", "")
if cuda_path:
cuda_bin = os.path.join(cuda_path, "bin")
if os.path.isdir(cuda_bin):
path_dirs.append(cuda_bin)
# Some CUDA installs put DLLs in bin\x64
cuda_bin_x64 = os.path.join(cuda_path, "bin", "x64")
if os.path.isdir(cuda_bin_x64):
path_dirs.append(cuda_bin_x64)
existing_path = env.get("PATH", "")
env["PATH"] = ";".join(path_dirs) + ";" + existing_path
else:
# Linux: set LD_LIBRARY_PATH for shared libs next to the binary
# and CUDA runtime libs (libcudart, libcublas, etc.)
import platform
lib_dirs = [binary_dir]
_arch = platform.machine() # x86_64, aarch64, etc.
# Pip-installed nvidia CUDA runtime libs (e.g. torch's
# bundled cuda-bindings). The prebuilt llama.cpp binary
# links against libcudart.so.13 / libcublas.so.13 which
# live here, not in /usr/local/cuda.
import glob as _glob
for _nv_pattern in [
os.path.join(
sys.prefix,
"lib",
"python*",
"site-packages",
"nvidia",
"cu*",
"lib",
),
os.path.join(
sys.prefix,
"lib",
"python*",
"site-packages",
"nvidia",
"cudnn",
"lib",
),
os.path.join(
sys.prefix,
"lib",
"python*",
"site-packages",
"nvidia",
"nvjitlink",
"lib",
),
]:
for _nv_dir in _glob.glob(_nv_pattern):
if os.path.isdir(_nv_dir):
lib_dirs.append(_nv_dir)
for cuda_lib in [
"/usr/local/cuda/lib64",
f"/usr/local/cuda/targets/{_arch}-linux/lib",
# Fallback CUDA compat paths (e.g. binary built with
# CUDA 12 on a system where default /usr/local/cuda
# points to CUDA 13+).
"/usr/local/cuda-12/lib64",
"/usr/local/cuda-12.8/lib64",
f"/usr/local/cuda-12/targets/{_arch}-linux/lib",
f"/usr/local/cuda-12.8/targets/{_arch}-linux/lib",
]:
if os.path.isdir(cuda_lib):
lib_dirs.append(cuda_lib)
existing_ld = env.get("LD_LIBRARY_PATH", "")
new_ld = ":".join(lib_dirs)
env["LD_LIBRARY_PATH"] = (
f"{new_ld}:{existing_ld}" if existing_ld else new_ld
)
# Pin to selected GPU(s). On ROCm, llama-server (and any torch
# in the subprocess) honors HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES;
# narrowing only CUDA_VISIBLE_DEVICES leaves an AMD child seeing
# the full HIP/ROCR set the parent inherited.
if gpu_indices is not None:
pinned = ",".join(str(i) for i in gpu_indices)
env["CUDA_VISIBLE_DEVICES"] = pinned
try:
import torch as _torch
if getattr(_torch.version, "hip", None) is not None:
env["HIP_VISIBLE_DEVICES"] = pinned
env["ROCR_VISIBLE_DEVICES"] = pinned
except Exception as e:
logger.debug(
"Failed to set ROCm visibility env vars for child: %s", e
)
# Defensive kill: if a concurrent load slipped past Phase 1
# (because its `self._process` was None at the time) and
# already stored a Popen handle here, drop that orphan
# before we overwrite the reference. See issue #5161.
self._kill_process()
self._stdout_lines = []
self._process = subprocess.Popen(
cmd,
stdout = subprocess.PIPE,
stderr = subprocess.STDOUT,
text = True,
env = env,
**_windows_hidden_subprocess_kwargs(),
)
# Start background thread to drain stdout and prevent pipe deadlock
self._stdout_thread = threading.Thread(
target = self._drain_stdout, daemon = True, name = "llama-stdout"
)
self._stdout_thread.start()
# Store the resolved on-disk path, not the caller's kwarg. In
# HF mode the caller passes gguf_path=None and the real path
# (``model_path``) is what llama-server is actually mmap'ing.
# Downstream consumers (load_progress, log lines, etc.) need
# the path that exists on disk.
self._gguf_path = model_path
self._hf_repo = hf_repo
# For local GGUF files, extract variant from filename if not provided
if hf_variant:
self._hf_variant = hf_variant
elif gguf_path:
try:
from utils.models.model_config import _extract_quant_label
self._hf_variant = _extract_quant_label(gguf_path)
except Exception:
self._hf_variant = None
else:
self._hf_variant = None
self._is_vision = is_vision
self._model_identifier = model_identifier
# Store the effective (possibly capped) context separately.
# Do NOT overwrite _context_length -- it holds the model's native
# context length from GGUF metadata and is used for display/info.
self._effective_context_length = (
effective_ctx if effective_ctx > 0 else self._context_length
)
self._max_context_length = (
max_available_ctx
if max_available_ctx > 0
else self._effective_context_length
)
# Wait for llama-server to become healthy
if not self._wait_for_health(timeout = 600.0):
self._kill_process()
_gguf = gguf_path or ""
_is_ollama = (
".studio_links" in _gguf
or os.sep + "ollama_links" + os.sep in _gguf
or os.sep + ".cache" + os.sep + "ollama" + os.sep in _gguf
or (self._model_identifier or "").startswith("ollama/")
)
# Only show the Ollama-specific message when the server
# output indicates a GGUF compatibility issue, not for
# unrelated failures like OOM or missing binaries.
if _is_ollama:
_output = "\n".join(self._stdout_lines[-50:]).lower()
_gguf_compat_hints = (
"key not found",
"unknown model architecture",
"failed to load model",
)
if any(h in _output for h in _gguf_compat_hints):
raise RuntimeError(
"Some Ollama models do not work with llama.cpp. "
"Try a different model, or use this model directly through Ollama instead."
)
raise RuntimeError(
"llama-server failed to start. "
"Check that the GGUF file is valid and you have enough memory."
)
self._healthy = True
logger.info(
f"llama-server ready on port {self._port} "
f"for model '{model_identifier}'"
)
return True
def unload_model(self) -> bool:
"""Terminate the llama-server subprocess and cancel any in-flight download."""
self._cancel_event.set()
with self._lock:
self._kill_process()
logger.info(f"Unloaded GGUF model: {self._model_identifier}")
self._model_identifier = None
self._gguf_path = None
self._hf_repo = None
self._hf_variant = None
self._is_vision = False
self._is_audio = False
self._audio_type = None
self._port = None
self._healthy = False
self._context_length = None
self._effective_context_length = None
self._max_context_length = None
self._chat_template = None
self._supports_reasoning = False
self._reasoning_always_on = False
self._reasoning_style = "enable_thinking"
self._reasoning_default = True
self._supports_preserve_thinking = False
self._supports_tools = False
self._cache_type_kv = None
self._speculative_type = None
self._n_layers = None
self._n_kv_heads = None
self._n_heads = None
self._embedding_length = None
self._kv_key_length = None
self._kv_value_length = None
self._sliding_window = None
self._full_attention_interval = None
self._kv_lora_rank = None
self._key_length_mla = None
self._ssm_inner_size = None
self._ssm_state_size = None
# Clean up temp chat template file
if hasattr(self, "_chat_template_file") and self._chat_template_file:
try:
import os
os.unlink(self._chat_template_file.name)
except Exception:
pass
self._chat_template_file = None
# Free audio codec GPU memory
if LlamaCppBackend._codec_mgr is not None:
LlamaCppBackend._codec_mgr.unload()
LlamaCppBackend._codec_mgr = None
import torch
if torch.cuda.is_available():
torch.cuda.empty_cache()
return True
def _kill_process(self):
"""Terminate the subprocess if running."""
if self._process is None:
return
try:
self._process.terminate()
self._process.wait(timeout = 5)
except subprocess.TimeoutExpired:
logger.warning("llama-server did not exit on SIGTERM, sending SIGKILL")
self._process.kill()
self._process.wait(timeout = 5)
except Exception as e:
logger.warning(f"Error killing llama-server process: {e}")
finally:
self._process = None
if self._stdout_thread is not None:
self._stdout_thread.join(timeout = 2)
self._stdout_thread = None
@staticmethod
def _kill_orphaned_servers():
"""Kill orphaned llama-server processes started by studio.
Only kills processes whose resolved binary lives under a known
Studio install directory (or matches an exact env-var override)
to avoid terminating unrelated llama-server instances.
Mirrors every location that _find_llama_server_binary() can
return from so that orphans from any supported install path
are still cleaned up.
Uses psutil for cross-platform support (Linux, macOS, Windows).
Falls back to pgrep + /proc/<pid>/exe on Linux when psutil is
not installed.
"""
import os
import signal
import sys
try:
# -- Build the ownership allowlist --------------------------------
# Two kinds of matches:
# exact_binaries -- env var overrides (exact path match only)
# install_roots -- directory trees that are Studio-owned
# (binary must be *under* one of these)
install_roots: list[Path] = []
# Primary install dir (setup.sh / prebuilt installer)
install_roots.append(Path.home() / ".unsloth" / "llama.cpp")
# Legacy in-tree build dirs (older setup.sh versions)
project_root = Path(__file__).resolve().parents[4]
install_roots.append(project_root / "llama.cpp")
# Legacy: extracted binary
install_roots.append(project_root / "bin")
# UNSLOTH_LLAMA_CPP_PATH env var (custom install dir)
custom_dir = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
if custom_dir:
install_roots.append(Path(custom_dir))
# LLAMA_SERVER_PATH env var (exact binary path)
exact_binaries: list[Path] = []
env_binary = os.environ.get("LLAMA_SERVER_PATH")
if env_binary:
try:
exact_binaries.append(Path(env_binary).resolve())
except OSError:
pass
# Resolve all roots so is_relative_to works reliably
resolved_roots: list[Path] = []
for root in install_roots:
try:
resolved_roots.append(root.resolve())
except OSError:
pass
my_pid = os.getpid()
# -- Enumerate processes -------------------------------------------
# Prefer psutil (cross-platform). Fall back to pgrep + /proc on
# Linux when psutil is not installed.
try:
import psutil
has_psutil = True
except ImportError:
has_psutil = False
if has_psutil:
for proc in psutil.process_iter(["pid", "name", "exe"]):
try:
if proc.info["pid"] == my_pid:
continue
name = proc.info.get("name") or ""
if not name.lower().startswith("llama-server"):
continue
exe = proc.info.get("exe")
if not exe:
continue
exe_path = Path(exe).resolve()
# Check ownership: exact binary match OR binary is
# under a known install root (proper ancestry, not
# substring).
is_ours = exe_path in exact_binaries or any(
exe_path.is_relative_to(root) for root in resolved_roots
)
if not is_ours:
continue
proc.kill()
logger.info(
f"Killed orphaned llama-server process "
f"(pid={proc.info['pid']})"
)
except (
psutil.NoSuchProcess,
psutil.AccessDenied,
psutil.ZombieProcess,
):
pass
else:
# -- Fallback: pgrep + /proc/<pid>/exe (Linux only) -----------
if sys.platform != "linux":
return
result = subprocess.run(
["pgrep", "-a", "-f", "llama-server"],
capture_output = True,
text = True,
timeout = 5,
)
if result.returncode != 0:
return
for line in result.stdout.strip().splitlines():
parts = line.strip().split(None, 1)
if len(parts) < 2:
continue
pid = int(parts[0])
if pid == my_pid:
continue
# Resolve the actual executable. /proc/<pid>/exe is a
# symlink to the real binary and avoids all cmdline-
# parsing ambiguities (spaces in paths, argv rewriting).
# Fall back to the first cmdline token when /proc is
# unavailable.
proc_exe = Path(f"/proc/{pid}/exe")
try:
binary = proc_exe.resolve(strict = True)
except (OSError, ValueError):
cmdline = parts[1]
token = cmdline.split()[0] if cmdline.strip() else ""
if not token:
continue
binary = Path(token).resolve(strict = False)
owned = binary in exact_binaries or any(
binary.is_relative_to(root) for root in resolved_roots
)
if not owned:
continue
try:
os.kill(pid, signal.SIGKILL)
logger.info(f"Killed orphaned llama-server process (pid={pid})")
except ProcessLookupError:
pass
except PermissionError:
pass
except Exception:
logger.warning("Error during orphan server cleanup", exc_info = True)
def _cleanup(self):
"""atexit handler to ensure llama-server is terminated."""
self._kill_process()
def _wait_for_health(self, timeout: float = 120.0, interval: float = 0.5) -> bool:
"""
Poll llama-server's /health endpoint until it responds 200.
Also monitors subprocess for early exit/crash.
"""
deadline = time.monotonic() + timeout
url = f"http://127.0.0.1:{self._port}/health"
while time.monotonic() < deadline:
# Check if process crashed
if self._process.poll() is not None:
# Give the drain thread a moment to collect final output
if self._stdout_thread is not None:
self._stdout_thread.join(timeout = 2)
output = "\n".join(self._stdout_lines[-50:])
logger.error(
f"llama-server exited with code {self._process.returncode}. "
f"Output: {output[:2000]}"
)
return False
try:
resp = httpx.get(url, timeout = 2.0)
if resp.status_code == 200:
return True
except (httpx.ConnectError, httpx.TimeoutException):
pass
time.sleep(interval)
logger.error(f"llama-server health check timed out after {timeout}s")
return False
# ── Message building (OpenAI format) ──────────────────────────
@staticmethod
def _parse_tool_calls_from_text(content: str) -> list[dict]:
"""
Parse tool calls from XML markup in content text.
Handles formats like:
<tool_call>{"name":"web_search","arguments":{"query":"..."}}</tool_call>
<tool_call><function=web_search><parameter=query>...</parameter></function></tool_call>
Closing tags (</tool_call>, </function>, </parameter>) are all optional
since models frequently omit them.
"""
tool_calls = []
# Pattern 1: JSON inside <tool_call> tags.
# Use balanced-brace extraction that skips braces inside JSON strings.
for m in _TC_JSON_START_RE.finditer(content):
brace_start = m.end() - 1 # position of the opening {
depth, i = 0, brace_start
in_string = False
while i < len(content):
ch = content[i]
if in_string:
if ch == "\\" and i + 1 < len(content):
i += 2 # skip escaped character
continue
if ch == '"':
in_string = False
elif ch == '"':
in_string = True
elif ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
break
i += 1
if depth == 0:
json_str = content[brace_start : i + 1]
try:
obj = json.loads(json_str)
tc = {
"id": f"call_{len(tool_calls)}",
"type": "function",
"function": {
"name": obj.get("name", ""),
"arguments": obj.get("arguments", {}),
},
}
if isinstance(tc["function"]["arguments"], dict):
tc["function"]["arguments"] = json.dumps(
tc["function"]["arguments"]
)
tool_calls.append(tc)
except (json.JSONDecodeError, ValueError):
pass
# Pattern 2: XML-style <function=name><parameter=key>value</parameter></function>
# All closing tags optional -- models frequently omit </parameter>,
# </function>, and/or </tool_call>.
if not tool_calls:
# Step 1: Find all <function=name> positions and extract their bodies.
# Body boundary: use only </tool_call> or next <function= as hard
# boundaries. We avoid using </function> as a boundary because
# code parameter values can contain that literal string.
# After extracting, we trim a trailing </function> if present.
func_starts = list(_TC_FUNC_START_RE.finditer(content))
for idx, fm in enumerate(func_starts):
func_name = fm.group(1)
body_start = fm.end()
# Hard boundaries: next <function= tag or </tool_call>
next_func = (
func_starts[idx + 1].start()
if idx + 1 < len(func_starts)
else len(content)
)
end_tag = _TC_END_TAG_RE.search(content[body_start:])
if end_tag:
body_end = body_start + end_tag.start()
else:
body_end = len(content)
body_end = min(body_end, next_func)
body = content[body_start:body_end]
# Trim trailing </function> if present (it's the real closing tag)
body = _TC_FUNC_CLOSE_RE.sub("", body)
# Step 2: Extract parameters from body.
# For single-parameter functions (the common case: code, command,
# query), use body end as the only boundary to avoid false matches
# on </parameter> inside code strings.
arguments = {}
param_starts = list(_TC_PARAM_START_RE.finditer(body))
if len(param_starts) == 1:
# Single parameter: value is everything from after the tag
# to end of body, trimming any trailing </parameter>.
pm = param_starts[0]
val = body[pm.end() :]
val = _TC_PARAM_CLOSE_RE.sub("", val)
arguments[pm.group(1)] = val.strip()
else:
for pidx, pm in enumerate(param_starts):
param_name = pm.group(1)
val_start = pm.end()
# Value ends at next <parameter= or end of body
next_param = (
param_starts[pidx + 1].start()
if pidx + 1 < len(param_starts)
else len(body)
)
val = body[val_start:next_param]
# Trim trailing </parameter> if present
val = _TC_PARAM_CLOSE_RE.sub("", val)
arguments[param_name] = val.strip()
tc = {
"id": f"call_{len(tool_calls)}",
"type": "function",
"function": {
"name": func_name,
"arguments": json.dumps(arguments),
},
}
tool_calls.append(tc)
return tool_calls
@staticmethod
def _build_openai_messages(
messages: list[dict],
image_b64: Optional[str] = None,
) -> list[dict]:
"""
Build OpenAI-format messages, optionally injecting an image_url
content part into the last user message for vision models.
If no image is provided, returns messages as-is.
"""
if not image_b64:
return messages
# Find the last user message and convert to multimodal content parts
result = [msg.copy() for msg in messages]
last_user_idx = None
for i, msg in enumerate(result):
if msg["role"] == "user":
last_user_idx = i
if last_user_idx is not None:
text_content = result[last_user_idx].get("content", "")
result[last_user_idx]["content"] = [
{"type": "text", "text": text_content},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{image_b64}",
},
},
]
return result
# ── Generation (proxy to llama-server) ────────────────────────
@staticmethod
def _iter_text_cancellable(
response: "httpx.Response",
cancel_event: Optional[threading.Event] = None,
) -> Generator[str, None, None]:
"""Iterate over an httpx streaming response with cancel support.
Checks cancel_event between chunks and on ReadTimeout. The
cancel watcher in _stream_with_retry also calls response.close()
on cancel, which unblocks iter_text() once the response exists.
During normal streaming llama-server sends tokens frequently,
so the cancel check between chunks is the primary mechanism.
"""
text_iter = response.iter_text()
while True:
if cancel_event is not None and cancel_event.is_set():
response.close()
return
try:
chunk = next(text_iter)
yield chunk
except StopIteration:
return
except httpx.ReadTimeout:
# No data within the timeout window -- just loop back
# and re-check cancel_event.
continue
@staticmethod
@contextlib.contextmanager
def _stream_with_retry(
client: "httpx.Client",
url: str,
payload: dict,
cancel_event: Optional[threading.Event] = None,
headers: Optional[dict] = None,
):
"""Open an httpx streaming POST with cancel support.
Sends the request once with a long read timeout (120 s) so
prompt processing (prefill) can finish without triggering a
retry storm. The previous 0.5 s timeout caused duplicate POST
requests every half second, forcing llama-server to restart
processing each time.
A background watcher thread provides cancel by closing the
response when cancel_event is set. Limitation: httpx does not
allow interrupting a blocked read from another thread before
the response object exists, so cancel during the initial
header wait (prefill phase) only takes effect once headers
arrive. After that, response.close() unblocks reads promptly.
In practice llama-server prefill is 1-5 s for typical prompts,
during which cancel is deferred -- still much better than the
old retry storm which made prefill slower.
"""
if cancel_event is not None and cancel_event.is_set():
raise GeneratorExit
# Background watcher: close the response if cancel is requested.
# Only effective after response headers arrive (httpx limitation).
_cancel_closed = threading.Event()
_response_ref: list = [None]
def _cancel_watcher():
while not _cancel_closed.is_set():
if cancel_event.wait(timeout = 0.3):
# Cancel requested. Keep polling until the response object
# exists so we can close it, or until the main thread
# finishes on its own (_cancel_closed is set in finally).
while not _cancel_closed.is_set():
r = _response_ref[0]
if r is not None:
try:
r.close()
return
except Exception as e:
logger.debug(
f"Error closing response in cancel watcher: {e}"
)
# Response not created yet -- wait briefly and retry
_cancel_closed.wait(timeout = 0.1)
return
watcher = None
if cancel_event is not None:
watcher = threading.Thread(
target = _cancel_watcher, daemon = True, name = "prefill-cancel"
)
watcher.start()
try:
# Long read timeout so prefill (prompt processing) can finish
# without triggering a retry storm. Cancel during both
# prefill and streaming is handled by the watcher thread
# which closes the response, unblocking any httpx read.
prefill_timeout = httpx.Timeout(
connect = 30,
read = 120.0,
write = 10,
pool = 10,
)
with client.stream(
"POST",
url,
json = payload,
timeout = prefill_timeout,
headers = headers,
) as response:
_response_ref[0] = response
if cancel_event is not None and cancel_event.is_set():
raise GeneratorExit
yield response
return
except (httpx.ReadError, httpx.RemoteProtocolError, httpx.CloseError):
# Response was closed by the cancel watcher
if cancel_event is not None and cancel_event.is_set():
raise GeneratorExit
raise
finally:
_cancel_closed.set()
def generate_chat_completion(
self,
messages: list[dict],
image_b64: Optional[str] = None,
temperature: float = 0.6,
top_p: float = 0.95,
top_k: int = 20,
min_p: float = 0.01,
max_tokens: Optional[int] = None,
repetition_penalty: float = 1.0,
presence_penalty: float = 0.0,
stop: Optional[list[str]] = None,
cancel_event: Optional[threading.Event] = None,
enable_thinking: Optional[bool] = None,
reasoning_effort: Optional[str] = None,
preserve_thinking: Optional[bool] = None,
) -> Generator[str | dict, None, None]:
"""
Send a chat completion request to llama-server and stream tokens back.
Uses /v1/chat/completions — llama-server handles chat template
application and vision (multimodal image_url parts) natively.
Yields cumulative text (matching InferenceBackend's convention).
"""
if not self.is_loaded:
raise RuntimeError("llama-server is not loaded")
openai_messages = self._build_openai_messages(messages, image_b64)
payload = {
"messages": openai_messages,
"stream": True,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k if top_k >= 0 else 0,
"min_p": min_p,
"repeat_penalty": repetition_penalty,
"presence_penalty": presence_penalty,
}
# Pass enable_thinking / reasoning_effort / preserve_thinking per-request
_reasoning_kw = self._request_reasoning_kwargs(
enable_thinking, reasoning_effort, preserve_thinking
)
if _reasoning_kw is not None:
payload["chat_template_kwargs"] = _reasoning_kw
# Default cap to the model's effective context length when known,
# otherwise the conservative floor. The wall-clock backstop below
# keeps a stuck model from running indefinitely either way.
payload["max_tokens"] = (
max_tokens
if max_tokens is not None
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
)
payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
if stop:
payload["stop"] = stop
payload["stream_options"] = {"include_usage": True}
url = f"{self.base_url}/v1/chat/completions"
cumulative = ""
in_thinking = False
_stream_done = False
_metadata_usage = None
_metadata_timings = None
try:
# _stream_with_retry uses a 120 s read timeout so prefill
# can finish. Cancel during streaming is handled by the
# watcher thread (closes the response on cancel_event).
stream_timeout = httpx.Timeout(connect = 10, read = 0.5, write = 10, pool = 10)
_auth_headers = (
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
)
with httpx.Client(
timeout = stream_timeout, limits = httpx.Limits(max_keepalive_connections = 0)
) as client:
with self._stream_with_retry(
client,
url,
payload,
cancel_event,
headers = _auth_headers,
) as response:
if response.status_code != 200:
error_body = response.read().decode()
raise RuntimeError(
f"llama-server returned {response.status_code}: {error_body}"
)
buffer = ""
has_content_tokens = False
reasoning_text = ""
for raw_chunk in self._iter_text_cancellable(
response, cancel_event
):
buffer += raw_chunk
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.strip()
if not line:
continue
if line == "data: [DONE]":
if in_thinking:
if has_content_tokens:
# Real thinking + content: close the tag
cumulative += "</think>"
yield cumulative
else:
# Only reasoning_content, no content tokens:
# the model put its entire reply in reasoning
# (e.g. Qwen3 always-think mode). Show it
# as the main response, not as a thinking block.
cumulative = reasoning_text
yield cumulative
_stream_done = True
break # exit inner while
if not line.startswith("data: "):
continue
try:
data = json.loads(line[6:])
# Capture server timings/usage from final chunks
_chunk_timings = data.get("timings")
if _chunk_timings:
_metadata_timings = _chunk_timings
_chunk_usage = data.get("usage")
if _chunk_usage:
_metadata_usage = _chunk_usage
choices = data.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
# Handle reasoning/thinking tokens
# llama-server sends these as "reasoning_content"
# Wrap in <think> tags for the frontend parser
reasoning = delta.get("reasoning_content", "")
if reasoning:
reasoning_text += reasoning
if not in_thinking:
cumulative += "<think>"
in_thinking = True
cumulative += reasoning
yield cumulative
token = delta.get("content", "")
if token:
has_content_tokens = True
if in_thinking:
cumulative += "</think>"
in_thinking = False
cumulative += token
yield cumulative
except json.JSONDecodeError:
logger.debug(
f"Skipping malformed SSE line: {line[:100]}"
)
if _stream_done:
break # exit outer for
if _metadata_usage or _metadata_timings:
yield {
"type": "metadata",
"usage": _metadata_usage,
"timings": _metadata_timings,
}
except httpx.ConnectError:
raise RuntimeError("Lost connection to llama-server")
except Exception as e:
if cancel_event is not None and cancel_event.is_set():
return
raise
# ── Tool-calling agentic loop ──────────────────────────────
def generate_chat_completion_with_tools(
self,
messages: list[dict],
tools: list[dict],
temperature: float = 0.6,
top_p: float = 0.95,
top_k: int = 20,
min_p: float = 0.01,
max_tokens: Optional[int] = None,
repetition_penalty: float = 1.0,
presence_penalty: float = 0.0,
stop: Optional[list[str]] = None,
cancel_event: Optional[threading.Event] = None,
enable_thinking: Optional[bool] = None,
reasoning_effort: Optional[str] = None,
preserve_thinking: Optional[bool] = None,
max_tool_iterations: int = 25,
auto_heal_tool_calls: bool = True,
tool_call_timeout: int = 300,
session_id: Optional[str] = None,
) -> Generator[dict, None, None]:
"""
Agentic loop: let the model call tools, execute them, and continue.
Yields dicts with:
{"type": "status", "text": "Searching: ..."/"Reading: ..."} -- tool status updates
{"type": "content", "text": "token"} -- streamed content tokens (cumulative)
{"type": "reasoning", "text": "token"} -- streamed reasoning tokens (cumulative)
"""
from core.inference.tools import execute_tool
if not self.is_loaded:
raise RuntimeError("llama-server is not loaded")
conversation = list(messages)
url = f"{self.base_url}/v1/chat/completions"
_accumulated_completion_tokens = 0
_accumulated_predicted_ms = 0.0
_accumulated_predicted_n = 0
def _strip_tool_markup(text: str, *, final: bool = False) -> str:
if not auto_heal_tool_calls:
return text
patterns = _TOOL_ALL_PATS if final else _TOOL_CLOSED_PATS
for pat in patterns:
text = pat.sub("", text)
return text.strip() if final else text
# XML prefixes that signal a tool call in content.
# Empty when auto_heal is disabled so the buffer never
# speculatively holds content for XML detection.
_TOOL_XML_SIGNALS = (
("<tool_call>", "<function=") if auto_heal_tool_calls else ()
)
_MAX_BUFFER_CHARS = 32
# ── Duplicate tool-call detection ────────────────────────
# Track recent (tool_name, arguments) hashes to detect loops
# where the model repeats the exact same call. Retries after
# a transient failure are allowed (only block when the previous
# identical call succeeded).
_tool_call_history: list[tuple[str, bool]] = [] # (key, failed)
# ── Re-prompt on plan-without-action ─────────────────
# When the model describes what it intends to do (forward-looking
# language) without actually calling a tool, re-prompt once.
# Only triggers on responses that signal intent/planning -- a
# direct answer like "4" or "Hello!" will not match.
# Pattern is compiled once at module level (_INTENT_SIGNAL).
_reprompt_count = 0
# Reserve extra iterations for re-prompts so they don't
# consume the caller's tool-call budget. Only add the
# extra slot when tool iterations are actually allowed.
_extra = _MAX_REPROMPTS if max_tool_iterations > 0 else 0
for iteration in range(max_tool_iterations + _extra):
if cancel_event is not None and cancel_event.is_set():
return
# Build payload -- stream: True so we detect tool signals
# in the first 1-2 chunks without a non-streaming penalty.
payload = {
"messages": conversation,
"stream": True,
"stream_options": {"include_usage": True},
"temperature": temperature,
"top_p": top_p,
"top_k": top_k if top_k >= 0 else 0,
"min_p": min_p,
"repeat_penalty": repetition_penalty,
"presence_penalty": presence_penalty,
"tools": tools,
"tool_choice": "auto",
}
_reasoning_kw = self._request_reasoning_kwargs(
enable_thinking, reasoning_effort, preserve_thinking
)
if _reasoning_kw is not None:
payload["chat_template_kwargs"] = _reasoning_kw
payload["max_tokens"] = (
max_tokens
if max_tokens is not None
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
)
payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
if stop:
payload["stop"] = stop
try:
_auth_headers = (
{"Authorization": f"Bearer {self._api_key}"}
if self._api_key
else None
)
# ── Speculative buffer state machine ──────────────────
# BUFFERING: accumulating content, checking for tool signals
# STREAMING: no tool detected, yielding tokens to caller
# DRAINING: tool signal found, silently consuming rest
_S_BUFFERING = 0
_S_STREAMING = 1
_S_DRAINING = 2
detect_state = _S_BUFFERING
content_buffer = "" # Raw content held during BUFFERING
content_accum = "" # All content tokens (for tool parsing)
reasoning_accum = ""
cumulative_display = "" # Cumulative text yielded (with <think>)
in_thinking = False
has_content_tokens = False
tool_calls_acc = {} # Structured delta.tool_calls fragments
has_structured_tc = False
_iter_usage = None
_iter_timings = None
_stream_done = False
_last_emitted = ""
stream_timeout = httpx.Timeout(
connect = 10,
read = 0.5,
write = 10,
pool = 10,
)
with httpx.Client(
timeout = stream_timeout,
limits = httpx.Limits(max_keepalive_connections = 0),
) as client:
with self._stream_with_retry(
client,
url,
payload,
cancel_event,
headers = _auth_headers,
) as response:
if response.status_code != 200:
error_body = response.read().decode()
raise RuntimeError(
f"llama-server returned {response.status_code}: "
f"{error_body}"
)
raw_buf = ""
for raw_chunk in self._iter_text_cancellable(
response,
cancel_event,
):
raw_buf += raw_chunk
while "\n" in raw_buf:
line, raw_buf = raw_buf.split("\n", 1)
line = line.strip()
if not line:
continue
if line == "data: [DONE]":
# Flush thinking state for STREAMING
if detect_state == _S_STREAMING and in_thinking:
if has_content_tokens:
cumulative_display += "</think>"
yield {
"type": "content",
"text": _strip_tool_markup(
cumulative_display,
final = True,
),
}
else:
cumulative_display = reasoning_accum
yield {
"type": "content",
"text": cumulative_display,
}
_stream_done = True
break # exit inner while
if not line.startswith("data: "):
continue
try:
chunk_data = json.loads(line[6:])
_ct = chunk_data.get("timings")
if _ct:
_iter_timings = _ct
_cu = chunk_data.get("usage")
if _cu:
_iter_usage = _cu
choices = chunk_data.get("choices", [])
if not choices:
continue
delta = choices[0].get("delta", {})
# ── Structured tool_calls ──
tc_deltas = delta.get("tool_calls")
if tc_deltas:
# Once visible content has been
# emitted, do not reclassify this
# turn as a tool call.
if _last_emitted:
continue
has_structured_tc = True
detect_state = _S_DRAINING
for tc_d in tc_deltas:
idx = tc_d.get("index", 0)
if idx not in tool_calls_acc:
tool_calls_acc[idx] = {
"id": tc_d.get("id", f"call_{idx}"),
"type": "function",
"function": {
"name": "",
"arguments": "",
},
}
elif tc_d.get("id"):
# Update ID if real one
# arrives on a later delta
tool_calls_acc[idx]["id"] = tc_d["id"]
func = tc_d.get("function", {})
if func.get("name"):
tool_calls_acc[idx]["function"][
"name"
] += func["name"]
if func.get("arguments"):
tool_calls_acc[idx]["function"][
"arguments"
] += func["arguments"]
continue
# ── Reasoning tokens ──
# Only yield in STREAMING state. In BUFFERING
# and DRAINING, accumulate silently so we don't
# corrupt the consumer's prev_text tracker
# (routes/inference.py never resets prev_text
# between tool iterations).
reasoning = delta.get("reasoning_content", "")
if reasoning:
reasoning_accum += reasoning
if detect_state == _S_STREAMING:
if not in_thinking:
cumulative_display += "<think>"
in_thinking = True
cumulative_display += reasoning
yield {
"type": "content",
"text": cumulative_display,
}
# ── Content tokens ──
token = delta.get("content", "")
if token:
has_content_tokens = True
content_accum += token
if detect_state == _S_DRAINING:
pass # accumulate silently
elif detect_state == _S_STREAMING:
if in_thinking:
cumulative_display += "</think>"
in_thinking = False
cumulative_display += token
cleaned = _strip_tool_markup(
cumulative_display,
)
if len(cleaned) > len(_last_emitted):
_last_emitted = cleaned
yield {
"type": "content",
"text": cleaned,
}
elif detect_state == _S_BUFFERING:
content_buffer += token
stripped_buf = content_buffer.lstrip()
if not stripped_buf:
continue
# Check tool signal prefixes
is_prefix = False
is_match = False
for sig in _TOOL_XML_SIGNALS:
if stripped_buf.startswith(sig):
is_match = True
break
if sig.startswith(stripped_buf):
is_prefix = True
break
if is_match:
detect_state = _S_DRAINING
elif (
is_prefix
and len(stripped_buf)
< _MAX_BUFFER_CHARS
):
pass # keep buffering
else:
# Not a tool -- flush buffer
detect_state = _S_STREAMING
# Flush any reasoning accumulated
# during BUFFERING phase
if reasoning_accum:
cumulative_display += "<think>"
cumulative_display += (
reasoning_accum
)
cumulative_display += "</think>"
cumulative_display += content_buffer
cleaned = _strip_tool_markup(
cumulative_display,
)
if len(cleaned) > len(_last_emitted):
_last_emitted = cleaned
yield {
"type": "content",
"text": cleaned,
}
except json.JSONDecodeError:
logger.debug(
f"Skipping malformed SSE line: " f"{line[:100]}"
)
if _stream_done:
break # exit outer for
# ── Resolve BUFFERING at stream end ──
if detect_state == _S_BUFFERING:
stripped_buf = content_buffer.lstrip()
if (
stripped_buf
and auto_heal_tool_calls
and any(s in stripped_buf for s in _TOOL_XML_SIGNALS)
):
detect_state = _S_DRAINING
elif content_accum or reasoning_accum:
detect_state = _S_STREAMING
if content_buffer:
# Flush any reasoning accumulated first
if reasoning_accum:
cumulative_display += "<think>"
cumulative_display += reasoning_accum
cumulative_display += "</think>"
cumulative_display += content_buffer
yield {
"type": "content",
"text": _strip_tool_markup(
cumulative_display,
final = True,
),
}
elif reasoning_accum and not has_content_tokens:
# Reasoning-only response (no content tokens):
# show reasoning as plain text, matching
# the final streaming pass behavior for
# models that put everything in reasoning.
cumulative_display = reasoning_accum
yield {
"type": "content",
"text": cumulative_display,
}
else:
return
# ── STREAMING path: no tool call ──
if detect_state == _S_STREAMING:
# Safety net: check for XML tool signals in content.
# The route layer resets prev_text on tool_start, so
# post-tool synthesis streams correctly even if
# content was already emitted before the tool XML.
_safety_tc = None
if auto_heal_tool_calls and any(
s in content_accum for s in _TOOL_XML_SIGNALS
):
_safety_tc = self._parse_tool_calls_from_text(
content_accum,
)
if not _safety_tc:
# ── Re-prompt on plan-without-action ──
# If the model described what it intends to do
# (forward-looking language) without calling any
# tool, nudge it to act. Only fires once per
# request and only on short responses that
# contain intent signals -- a direct answer
# like "4" or "Hello!" won't trigger this.
# Use content if available, otherwise fall back
# to reasoning text (reasoning-only stalls).
_stripped = content_accum.strip()
if not _stripped:
_stripped = reasoning_accum.strip()
if (
tools
and _reprompt_count < _MAX_REPROMPTS
and 0 < len(_stripped) < _REPROMPT_MAX_CHARS
and _INTENT_SIGNAL.search(_stripped)
):
_reprompt_count += 1
logger.info(
f"Re-prompt {_reprompt_count}/{_MAX_REPROMPTS}: "
f"model responded without calling tools "
f"({len(_stripped)} chars)"
)
conversation.append(
{
"role": "assistant",
"content": _stripped,
}
)
conversation.append(
{
"role": "user",
"content": (
"STOP. Do NOT write code or explain. "
"You MUST call a tool NOW. "
"Call web_search or python immediately."
),
}
)
# Accumulate tokens and timing from this iteration
_fu_r = _iter_usage or {}
_accumulated_completion_tokens += _fu_r.get(
"completion_tokens", 0
)
_it_r = _iter_timings or {}
_accumulated_predicted_ms += _it_r.get("predicted_ms", 0)
_accumulated_predicted_n += _it_r.get("predicted_n", 0)
yield {"type": "status", "text": ""}
continue
# Content was already streamed. Yield metadata.
yield {"type": "status", "text": ""}
_fu = _iter_usage or {}
_fc = _fu.get("completion_tokens", 0)
_fp = _fu.get("prompt_tokens", 0)
_tc = _fc + _accumulated_completion_tokens
if (
_iter_usage
or _iter_timings
or _accumulated_completion_tokens
):
_mt = dict(_iter_timings) if _iter_timings else {}
if _accumulated_predicted_ms or _accumulated_predicted_n:
_mt["predicted_ms"] = (
_mt.get("predicted_ms", 0)
+ _accumulated_predicted_ms
)
_tn = (
_mt.get("predicted_n", 0) + _accumulated_predicted_n
)
_mt["predicted_n"] = _tn
_tms = _mt["predicted_ms"]
if _tms > 0:
_mt["predicted_per_second"] = _tn / (_tms / 1000.0)
yield {
"type": "metadata",
"usage": {
"prompt_tokens": _fp,
"completion_tokens": _tc,
"total_tokens": _fp + _tc,
},
"timings": _mt,
}
return
# Safety net caught tool XML -- treat as tool call
tool_calls = _safety_tc
content_text = _strip_tool_markup(
content_accum,
final = True,
)
logger.info(
f"Safety net: parsed {len(tool_calls)} tool call(s) "
f"from streamed content"
)
else:
# ── DRAINING path: assemble tool_calls ──
tool_calls = None
content_text = content_accum
if has_structured_tc:
# Filter out incomplete fragments (e.g. from
# truncation by max_tokens or disconnect).
tool_calls = [
tool_calls_acc[i]
for i in sorted(tool_calls_acc)
if (
tool_calls_acc[i]
.get("function", {})
.get("name", "")
.strip()
)
] or None
if (
not tool_calls
and auto_heal_tool_calls
and any(s in content_accum for s in _TOOL_XML_SIGNALS)
):
tool_calls = self._parse_tool_calls_from_text(
content_accum,
)
if tool_calls and not has_structured_tc:
content_text = _strip_tool_markup(
content_text,
final = True,
)
if tool_calls:
logger.info(
f"Parsed {len(tool_calls)} tool call(s) from "
f"{'structured delta' if has_structured_tc else 'content text'}"
)
if not tool_calls:
# DRAINING but no tool calls (false positive).
# Merge accumulated metrics from prior tool
# iterations so they are not silently dropped.
yield {"type": "status", "text": ""}
if content_accum:
# Strip leaked tool-call XML before yielding
content_accum = _strip_tool_markup(
content_accum, final = True
)
if content_accum:
yield {"type": "content", "text": content_accum}
_fu = _iter_usage or {}
_fc = _fu.get("completion_tokens", 0)
_fp = _fu.get("prompt_tokens", 0)
_tc = _fc + _accumulated_completion_tokens
if (
_iter_usage
or _iter_timings
or _accumulated_completion_tokens
):
_mt = dict(_iter_timings) if _iter_timings else {}
if _accumulated_predicted_ms or _accumulated_predicted_n:
_mt["predicted_ms"] = (
_mt.get("predicted_ms", 0)
+ _accumulated_predicted_ms
)
_tn = (
_mt.get("predicted_n", 0) + _accumulated_predicted_n
)
_mt["predicted_n"] = _tn
_tms = _mt["predicted_ms"]
if _tms > 0:
_mt["predicted_per_second"] = _tn / (_tms / 1000.0)
yield {
"type": "metadata",
"usage": {
"prompt_tokens": _fp,
"completion_tokens": _tc,
"total_tokens": _fp + _tc,
},
"timings": _mt,
}
return
# ── Execute tool calls ──
_accumulated_completion_tokens += (_iter_usage or {}).get(
"completion_tokens", 0
)
_it = _iter_timings or {}
_accumulated_predicted_ms += _it.get("predicted_ms", 0)
_accumulated_predicted_n += _it.get("predicted_n", 0)
assistant_msg = {"role": "assistant", "content": content_text}
if tool_calls:
assistant_msg["tool_calls"] = tool_calls
conversation.append(assistant_msg)
for tc in tool_calls or []:
func = tc.get("function", {})
tool_name = func.get("name", "")
raw_args = func.get("arguments", {})
if isinstance(raw_args, str):
try:
arguments = json.loads(raw_args)
except (json.JSONDecodeError, ValueError):
if auto_heal_tool_calls:
arguments = {"query": raw_args}
else:
arguments = {"raw": raw_args}
else:
arguments = raw_args
if tool_name == "web_search":
_ws_url = (arguments.get("url") or "").strip()
if _ws_url:
_parsed = urlparse(_ws_url)
if _parsed.scheme in ("http", "https") and _parsed.hostname:
_ws_host = _parsed.hostname
if _ws_host.startswith("www."):
_ws_host = _ws_host[4:]
status_text = f"Reading: {_ws_host}"
else:
status_text = "Reading page..."
else:
status_text = f"Searching: {arguments.get('query', '')}"
elif tool_name == "python":
preview = (
(arguments.get("code") or "").strip().split("\n")[0][:60]
)
status_text = (
f"Running Python: {preview}"
if preview
else "Running Python..."
)
elif tool_name == "terminal":
cmd_preview = (arguments.get("command") or "")[:60]
status_text = (
f"Running: {cmd_preview}"
if cmd_preview
else "Running command..."
)
else:
status_text = f"Calling: {tool_name}"
yield {"type": "status", "text": status_text}
yield {
"type": "tool_start",
"tool_name": tool_name,
"tool_call_id": tc.get("id", ""),
"arguments": arguments,
}
# ── Duplicate call detection ──────────────
# str(dict) is stable here: arguments always comes from
# json.loads on the same model output within one request,
# so insertion order is deterministic (Python 3.7+).
_tc_key = tool_name + str(arguments)
_prev = _tool_call_history[-1] if _tool_call_history else None
if _prev and _prev[0] == _tc_key and not _prev[1]:
result = (
"You already made this exact call. "
"Do not repeat the same tool call. "
"Try a different approach: fetch a URL "
"from previous results, use Python to "
"process data you already have, or "
"provide your final answer now."
)
else:
_effective_timeout = (
None if tool_call_timeout >= 9999 else tool_call_timeout
)
result = execute_tool(
tool_name,
arguments,
cancel_event = cancel_event,
timeout = _effective_timeout,
session_id = session_id,
)
yield {
"type": "tool_end",
"tool_name": tool_name,
"tool_call_id": tc.get("id", ""),
"result": result,
}
# Nudge model to try a different approach on errors
_error_prefixes = (
"Error",
"Search failed",
"Execution error",
"Blocked:",
"Exit code",
"Failed to fetch",
"Failed to resolve",
"No query provided",
)
_is_error = isinstance(result, str) and result.lstrip().startswith(
_error_prefixes
)
_tool_call_history.append((_tc_key, _is_error))
# Strip image sentinel before feeding result to the LLM
# (the full result with sentinel is still yielded via
# tool_end so the frontend can extract image paths).
_result_content = result
if "\n__IMAGES__:" in _result_content:
_result_content = _result_content.rsplit("\n__IMAGES__:", 1)[0]
if _is_error:
_result_content = (
_result_content + "\n\nThe tool call encountered an issue. "
"Please try a different approach or rephrase your request."
)
tool_msg = {
"role": "tool",
"name": tool_name,
"content": _result_content,
}
tool_call_id = tc.get("id")
if tool_call_id:
tool_msg["tool_call_id"] = tool_call_id
conversation.append(tool_msg)
# Clear tool status badge before next generation iteration
yield {"type": "status", "text": ""}
# Continue the loop to let model respond with context
continue
except httpx.ConnectError:
raise RuntimeError("Lost connection to llama-server")
except Exception as e:
if cancel_event is not None and cancel_event.is_set():
return
raise
# ── Tool iteration cap reached -- synthesize final answer ──
# The model used all iterations without producing a final text
# response. Inject a nudge so the final streaming pass produces
# a useful answer instead of continuing to request tools.
if max_tool_iterations > 0:
conversation.append(
{
"role": "user",
"content": (
"You have used all available tool calls. Based on "
"everything you have found so far, provide your final "
"answer now. Do not call any more tools."
),
}
)
# Clear status
yield {"type": "status", "text": ""}
# Final streaming pass with the full conversation context
stream_payload = {
"messages": conversation,
"stream": True,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k if top_k >= 0 else 0,
"min_p": min_p,
"repeat_penalty": repetition_penalty,
"presence_penalty": presence_penalty,
}
_reasoning_kw = self._request_reasoning_kwargs(
enable_thinking, reasoning_effort, preserve_thinking
)
if _reasoning_kw is not None:
stream_payload["chat_template_kwargs"] = _reasoning_kw
stream_payload["max_tokens"] = (
max_tokens
if max_tokens is not None
else (self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR)
)
stream_payload["t_max_predict_ms"] = _DEFAULT_T_MAX_PREDICT_MS
if stop:
stream_payload["stop"] = stop
stream_payload["stream_options"] = {"include_usage": True}
cumulative = ""
_last_emitted = ""
in_thinking = False
has_content_tokens = False
reasoning_text = ""
_metadata_usage = None
_metadata_timings = None
_stream_done = False
try:
stream_timeout = httpx.Timeout(connect = 10, read = 0.5, write = 10, pool = 10)
_auth_headers = (
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
)
with httpx.Client(
timeout = stream_timeout, limits = httpx.Limits(max_keepalive_connections = 0)
) as client:
with self._stream_with_retry(
client,
url,
stream_payload,
cancel_event,
headers = _auth_headers,
) as response:
if response.status_code != 200:
error_body = response.read().decode()
raise RuntimeError(
f"llama-server returned {response.status_code}: {error_body}"
)
buffer = ""
for raw_chunk in self._iter_text_cancellable(
response, cancel_event
):
buffer += raw_chunk
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.strip()
if not line:
continue
if line == "data: [DONE]":
if in_thinking:
if has_content_tokens:
cumulative += "</think>"
yield {
"type": "content",
"text": _strip_tool_markup(
cumulative, final = True
),
}
else:
cumulative = reasoning_text
yield {"type": "content", "text": cumulative}
_stream_done = True
break # exit inner while
if not line.startswith("data: "):
continue
try:
chunk_data = json.loads(line[6:])
# Capture server timings/usage from final chunks
_chunk_timings = chunk_data.get("timings")
if _chunk_timings:
_metadata_timings = _chunk_timings
_chunk_usage = chunk_data.get("usage")
if _chunk_usage:
_metadata_usage = _chunk_usage
choices = chunk_data.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
reasoning = delta.get("reasoning_content", "")
if reasoning:
reasoning_text += reasoning
if not in_thinking:
cumulative += "<think>"
in_thinking = True
cumulative += reasoning
yield {"type": "content", "text": cumulative}
token = delta.get("content", "")
if token:
has_content_tokens = True
if in_thinking:
cumulative += "</think>"
in_thinking = False
cumulative += token
cleaned = _strip_tool_markup(cumulative)
# Only emit when cleaned text grows (monotonic).
if len(cleaned) > len(_last_emitted):
_last_emitted = cleaned
yield {"type": "content", "text": cleaned}
except json.JSONDecodeError:
logger.debug(
f"Skipping malformed SSE line: {line[:100]}"
)
if _stream_done:
break # exit outer for
_final_usage = _metadata_usage or {}
_final_completion = _final_usage.get("completion_tokens", 0)
_final_prompt = _final_usage.get("prompt_tokens", 0)
_total_completion = (
_final_completion + _accumulated_completion_tokens
)
if _metadata_usage or _metadata_timings:
_merged_timings = (
dict(_metadata_timings) if _metadata_timings else {}
)
if _accumulated_predicted_ms or _accumulated_predicted_n:
_merged_timings["predicted_ms"] = (
_merged_timings.get("predicted_ms", 0)
+ _accumulated_predicted_ms
)
_total_predicted_n = (
_merged_timings.get("predicted_n", 0)
+ _accumulated_predicted_n
)
_merged_timings["predicted_n"] = _total_predicted_n
_total_predicted_ms = _merged_timings["predicted_ms"]
if _total_predicted_ms > 0:
_merged_timings["predicted_per_second"] = (
_total_predicted_n / (_total_predicted_ms / 1000.0)
)
yield {
"type": "metadata",
"usage": {
"prompt_tokens": _final_prompt,
"completion_tokens": _total_completion,
"total_tokens": _final_prompt + _total_completion,
},
"timings": _merged_timings,
}
except httpx.ConnectError:
raise RuntimeError("Lost connection to llama-server")
except Exception as e:
if cancel_event is not None and cancel_event.is_set():
return
raise
# ── TTS support ────────────────────────────────────────────
def detect_audio_type(self) -> Optional[str]:
"""Detect audio/TTS codec by probing the loaded model's vocabulary."""
if not self.is_loaded:
return None
try:
_auth_headers = (
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
)
with httpx.Client(timeout = 10, headers = _auth_headers) as client:
def _detok(tid: int) -> str:
r = client.post(
f"{self.base_url}/detokenize", json = {"tokens": [tid]}
)
return r.json().get("content", "") if r.status_code == 200 else ""
def _tok(text: str) -> list[int]:
r = client.post(
f"{self.base_url}/tokenize",
json = {"content": text, "add_special": False},
)
return r.json().get("tokens", []) if r.status_code == 200 else []
# Check codec-specific tokens (not generic ones that may exist in non-audio models)
if "<custom_token_" in _detok(128258) and "<custom_token_" in _detok(
128259
):
return "snac"
if len(_tok("<|AUDIO|>")) == 1 and len(_tok("<|audio_eos|>")) == 1:
return "csm"
if len(_tok("<|startoftranscript|>")) == 1:
return "whisper"
if (
len(_tok("<|bicodec_semantic_0|>")) == 1
and len(_tok("<|bicodec_global_0|>")) == 1
):
return "bicodec"
if len(_tok("<|c1_0|>")) == 1 and len(_tok("<|c2_0|>")) == 1:
return "dac"
except Exception as e:
logger.debug(f"Audio type detection failed: {e}")
return None
# Prompt format per codec: (template, stop_tokens, needs_token_ids)
# Matches prompts in InferenceBackend._generate_snac/bicodec/dac
_TTS_PROMPTS = {
"snac": (
"<custom_token_3>{text}<|eot_id|><custom_token_4>",
["<custom_token_2>"],
True,
),
"bicodec": (
"<|task_tts|><|start_content|>{text}<|end_content|><|start_global_token|>",
["<|im_end|>", "</s>"],
False,
),
"dac": (
"<|im_start|>\n<|text_start|>{text}<|text_end|>\n<|audio_start|><|global_features_start|>\n",
["<|im_end|>", "<|audio_end|>"],
False,
),
}
_codec_mgr = None # Shared AudioCodecManager instance
def init_audio_codec(self, audio_type: str) -> None:
"""Load the audio codec at model load time (mirrors non-GGUF path)."""
import torch
from core.inference.audio_codecs import AudioCodecManager
if LlamaCppBackend._codec_mgr is None:
LlamaCppBackend._codec_mgr = AudioCodecManager()
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_path = None
# BiCodec needs a repo with BiCodec/ weights — download canonical SparkTTS
if audio_type == "bicodec":
from huggingface_hub import snapshot_download
import os
repo_path = snapshot_download(
"unsloth/Spark-TTS-0.5B", local_dir = "Spark-TTS-0.5B"
)
model_repo_path = os.path.abspath(repo_path)
LlamaCppBackend._codec_mgr.load_codec(
audio_type, device, model_repo_path = model_repo_path
)
logger.info(f"Loaded audio codec for GGUF TTS: {audio_type}")
def generate_audio_response(
self,
text: str,
audio_type: str,
temperature: float = 0.6,
top_p: float = 0.95,
top_k: int = 50,
min_p: float = 0.0,
max_new_tokens: int = 2048,
repetition_penalty: float = 1.1,
) -> tuple:
"""
Generate TTS audio via llama-server /completion + codec decoding.
Returns (wav_bytes, sample_rate).
"""
if audio_type not in self._TTS_PROMPTS:
raise RuntimeError(f"GGUF TTS does not support '{audio_type}' codec.")
tpl, stop, need_ids = self._TTS_PROMPTS[audio_type]
payload: dict = {
"prompt": tpl.format(text = text),
"stream": False,
"n_predict": max_new_tokens,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k if top_k >= 0 else 0,
"min_p": min_p,
"repeat_penalty": repetition_penalty,
}
if stop:
payload["stop"] = stop
if need_ids:
payload["n_probs"] = 1
_auth_headers = (
{"Authorization": f"Bearer {self._api_key}"} if self._api_key else None
)
with httpx.Client(
timeout = httpx.Timeout(300, connect = 10), headers = _auth_headers
) as client:
resp = client.post(f"{self.base_url}/completion", json = payload)
if resp.status_code != 200:
raise RuntimeError(
f"llama-server returned {resp.status_code}: {resp.text}"
)
data = resp.json()
token_ids = (
[p["id"] for p in data.get("completion_probabilities", []) if "id" in p]
if need_ids
else None
)
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
return LlamaCppBackend._codec_mgr.decode(
audio_type, device, token_ids = token_ids, text = data.get("content", "")
)
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