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* model : add support for HrmTextForCausalLM (DFM Mimir 1B)
HRM-Text runs two transformer stacks (low, high) in an alternating cycle over the same token stream. The low-cycle state z_l starts from a learned [n_embd] tensor and is broadcast over positions.
- conversion: new writer for the fused gqkv projection (order gate,q,k,v) remapped to llama.cpp q/k/v plus a separate sigmoid gate tensor
- loader: block_count = lps * h_cycles * (l_cycles + 1) cache slots aliasing 2*lps physical blocks via struct copies
- graph: looped build with sigmoid-gated attention, SwiGLU FFN and parameterless RMS norms; learned embedding_scale applied in build_inp_embd
- saver: pointer-deduplicated layer loop (looped archs alias tensors)
- tests: hrm_text fixture (lps 1, h 2, l 3) in test-llama-archs
Limitations:
causal attention only - the upstream prefix-LM mode is not implemented (the prefix_lm GGUF key round-trips unused).
The KV cache holds one entry per pass: 128 layers for Mimir 1B, i.e. 4x a same-width 32-layer model - about 3072 MiB at ctx 4096 in F16 (halves with q8_0 KV + FA).
Every token runs all 128 block passes, so decode cost is roughly 4x a dense model of equal width (2.65 t/s BF16, 8-thread desktop CPU).
Verified against the HF reference: identical argmax at 334/334 positions across 20 prompts (BF16 GGUF vs FP32 golden).
q8_0 requant: 95.8% top-1, all remaining misses inside the HF top-5 (accumulated error over 128 sequential blocks).
AI usage disclosure: YES
Used GLM-5.3 for the majority of code AI-generated under my direction, all gates verified locally.
All in all I could say that I have written less than 20% of the code and most of the heavy lifting has been done by the model. As such, this should be considered experimental.
* Update conversion/hrm_text.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-arch.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* convert : add gguf_writer methods for hrm_text metadata
replace raw add_uint32/add_bool calls with dedicated GGUFWriter methods, following the add_embedding_scale pattern
Assisted-by: GLM-5.3
* convert : map regular hrm_text tensors via tensor_mapping
delegate unfused checkpoints to the base tensor mapping; training-style attn. names are renamed to self_attn. so the patterns match
Assisted-by: GLM-5.3
* model : format hrm-text build_* calls as in other models
one argument group per line, matching sibling model files
Assisted-by: GLM-5.3
* llama : move hrm z_l_init table entries out of the nemotron group
place the name and tensor-info entries with the other global input tensors
Assisted-by: GLM-5.3
* convert : slim down hrm_text comments
Assisted-by: GLM-5.3
* convert : build hrm_text block tensor names from the {bid} template
The tensor map holds concrete per-block names, so format the template
with the computed layer index before handing it to super().
* llama : name hrm metadata keys in their own hrm. namespace
The four keys are arch-independent, unlike the arch-substituted
Keys.LLM entries, so group them under Keys.HRM (like Keys.Split) and
rename the llm_kv entries to LLM_KV_HRM_*. Only our own GGUFs carry
the old hrm_text.* keys; they are regenerated.
* Update src/llama-model-saver.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* llama : keep hrm metadata keys arch-substituted
Per review: the GGUF keys stay "{arch}.h_cycles" style, so the Python
members drop the LLM_KV_HRM_ prefix and keep arch templates; C++ keeps
the LLM_KV_HRM_* enums. GGUF output is unchanged - existing files and
HF uploads stay valid.
* Update gguf-py/gguf/constants.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-arch.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-arch.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* convert : rename hrm writer methods to add_hrm_*
Generic names like add_h_cycles/add_prefix_lm are too broad on the
shared GGUFWriter; prefix them with hrm_ like the metadata keys.
* model : fix meta-split lookup for archs with aliased cache slots
Cache tensors of archs that alias physical blocks across looped slots
(hrm_text, nanbeige with num_loops > 1) can reference block indices
without weight tensor names. Take the output projection from the layer
array instead of asserting; all other lookups are unchanged.
* model : replicate hrm_text tensors on meta devices instead of splitting
The aliased cache slots rotate split states differently from their
physical weights, so the meta-split execution invariants (set_rows
requires the cache state to match the token indices) cannot hold for
any device count. Replicate all hrm_text tensors on every meta device
instead; single-device and non-meta paths are unchanged.
Assisted-by: Claude Sonnet
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
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| benches | ||
| ci | ||
| cmake | ||
| common | ||
| conversion | ||
| docs | ||
| examples | ||
| ggml | ||
| gguf-py | ||
| grammars | ||
| include | ||
| licenses | ||
| media | ||
| models | ||
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| scripts | ||
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| AGENTS.md | ||
| AUTHORS | ||
| build-xcframework.sh | ||
| CLAUDE.md | ||
| CMakeLists.txt | ||
| CMakePresets.json | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| convert_hf_to_gguf.py | ||
| convert_hf_to_gguf_update.py | ||
| convert_llama_ggml_to_gguf.py | ||
| convert_lora_to_gguf.py | ||
| flake.nix | ||
| LICENSE | ||
| Makefile | ||
| mypy.ini | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| README.md | ||
| requirements.txt | ||
| SECURITY.md | ||
| ty.toml | ||
llama.cpp
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF
# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
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Description
The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
a wide range of hardware - locally and in the cloud.
- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
The llama.cpp project is build on top of the ggml library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cpprepo and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain