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agentaideepseekdeepseek-r1fine-tuninggemmagemma3llamallama3llmllmsloramistralopenaipytorchqwenqwen3text-to-speechttsunsloth
649b9f7808
* Studio: expose --parallel / -np on `unsloth studio run`
The CLI was hardcoding `llama_parallel_slots=4` in `run_kwargs` at
`unsloth_cli/commands/studio.py`, leaving users unable to tune the
concurrent decode slot count even though the engine, KV-cache math,
and `studio.backend.run.run_server(llama_parallel_slots=...)`
plumbing all already accepted any N. This change adds a `--parallel`
/ `--n-parallel` / `-np` typer option (default 4 -- matches the
previous hardcoded value), forwards it into `run_kwargs`, and pins
the new surface with 4 unit tests.
Per-request state in `routes/inference.py` is already isolated
(`cancel_event` and `prev_text` are per-request locals in every
streaming handler; the `_lock` / `_serial_load_lock` only wrap
load/unload, not chat completions), so no concurrency refactor is
needed alongside this -- the engine layer already handles N
concurrent requests on one loaded model when llama-server is told
to.
Range guards: 1 <= N <= 64. With higher N each slot gets ctx/N KV
cache; users tuning this should be aware that per-call context
shrinks proportionally.
`unsloth studio` (the bare default command, no subcommand) still
defaults to llama_parallel_slots=1 via `run_server`'s own default;
this PR does not change that path -- it only exposes the knob on the
one-liner `studio run` command that already silently used 4.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Forward --parallel through venv re-exec and drop colliding short aliases
`unsloth studio run` re-execs into the Studio venv when invoked from
outside it (the common path). The arg-builder forwards every typer
option but the new --parallel, so the child re-execs at the default 4
and any user value is silently dropped. Worse: pre-PR users who
already pass `-np N` as a pass-through extra (where llama.cpp's
last-wins parsing made it stick) silently lose N after this PR lands.
Forward --parallel explicitly in the re-exec arg list.
While auditing the re-exec path, also drop the colliding 1-char
short aliases -m (--model) and -f (--frontend) plus the redundant
-hfr. Click's short-option clustering had been silently mis-parsing
~11 llama-server short flags via the pass-through path: -fa as
`-f a`, -mg 0 as `-m g` + stray 0, -fitt 1024 as `-f itt` + stray
1024, -hff path as `-f f` + stray `-h path`, -cmoe / -cram / -sm /
-ncmoe etc. The docstring promise ("any flag this command does not
recognize is forwarded verbatim") was silently violated.
-hf (2-char) is kept because Click treats multi-char shorts atomically
(no clustering of -hff / -hfv / -hffv / -hft) and -hf is documented
in basics/api/README.md. --model / --hf-repo / --frontend long forms
all unchanged. studio_default keeps -f because it has no pass-through.
Tests:
- test_studio_run_parallel_flag.py: 8 new re-exec coverage cases
(all 3 aliases, 3 platforms via sys.platform mock, pre-PR `-np`
regression, mixed with pass-through extras).
- test_studio_run_short_alias_clashes.py (new): surface checks that
the removed shorts cannot reappear, plus 11 parametrized cases
proving each previously-broken llama-server short flag now passes
through verbatim, plus a happy-path test that documented -hf still
works for `org/repo:variant` syntax.
All 27 tests pass. Negative test (revert either fix) shows the new
tests catch the regression.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix stale studio run docstring describing rejected llama-server flags
The pre-PR docstring listed --port, -c / --ctx-size, --api-key, -ngl,
--jinja, --flash-attn, --no-context-shift as "rejected with HTTP 400",
but only --port and --api-key (plus other networking / auth / model
identity / single-model UI flags) are actually in
studio/backend/core/inference/llama_server_args.py's denylist. -c /
-ngl / --jinja / --flash-attn / --no-context-shift are pass-through
and last-wins-override Studio's auto-set value.
Rewrite the docstring to match the real denylist groups and point at
the canonical source. Also add --parallel to one of the examples now
that it is a first-class flag.
* ci: broaden Linux + narrow Windows llama.cpp runtime patterns + trim #5741 comments (#5746)
* ci: broaden Linux llama.cpp runtime pattern to lib*.so*
#5741 patched the explicit Linux pattern list to add
``libllama-*-impl.so*`` after ggml-org/llama.cpp#23462 (between
b9279 and b9283) split each binary's entry code into a paired
``lib<binary>-impl.so`` shared library. Same class of upstream
repackaging will hit us again whenever a new shared lib is added.
Mirror what macOS already does and replace the per-lib list with a
single ``lib*.so*`` glob. ``copy_globs`` (line 3614) unions
patterns, so the per-variant ``libggml-cuda.so*`` / ``libggml-hip.so*``
entries were never filtering anything; the spec lives in
``runtime_payload_health_groups`` (line 5209) which keeps the
explicit minimum-required list per variant.
Dry-run against b9296-bin-ubuntu-x64.tar.gz: 40 files copied (all
ggml, llama, mtmd, impl variants + the two binaries we ship), 22
skipped (other CLIs, rpc-server, LICENSE). Functionally equal to
the post-#5741 set.
* cleanup: trim #5741 comments on the pydantic split
Comments added in #5741 explained the original bug in full each
time. They are mostly redundant with the commit message and the PR.
Trim them to one short paragraph per site.
No behavior change.
* ci: narrow Windows runtime pattern to llama-server.exe + llama-quantize.exe
Studio only invokes llama-server and llama-quantize. Mac and Linux
already filter to those two binaries; Windows was the odd one out
with ``*.exe`` copying every CLI upstream ships (llama-cli,
llama-bench, llama-mtmd-cli, ...).
Dry-run on b9296 (win cpu-x64, cpu-arm64, cuda-13.1, hip-radeon):
20 unused EXEs skipped per variant, all DLLs (incl. the new
llama-*-impl.dll family) still copied via ``*.dll``.
``existing_install_matches_choice`` already checks llama-server.exe
exists explicitly (line 5297), so the health gate is unchanged.
* Lower default weight_decay in RL config from 0.01 to 0.001 (#5747)
In full FT, AdamW weight decay shrinks the parameter directly so the
implicit prior is W -> 0. In LoRA the trained parameters are A and B
while the effective weight is W = W_init + (alpha/r) * B @ A; decaying
A and B separately drives BA -> 0, hence W -> W_init rather than 0.
The previous default of 0.01 inherited from full-FT recipes adds a
measurable pull on the merged adapter back toward the base model over
a few thousand steps. 0.001 keeps a small Frobenius-norm prior on
||A||^2 + ||B||^2 for numerical stability without meaningfully biasing
the merged weight toward init, and aligns with the value used across
the unsloth notebook templates.
* Studio: strip orphan tool_call XML leaking into visible content (#5735)
* Studio: strip orphan tool_call XML from streamed visible content
The speculative-buffer state machine in
`studio/backend/core/inference/llama_cpp.py` can slice a tool_call XML
block between the silent DRAINING path and the user-visible
content_accum, depending on when in the model's emission the BUFFERING
-> STREAMING -> DRAINING transitions fire. Three leak shapes were
observed in a 2026-05-22 sweep of 900 Qwen3.5 / Qwen3.6 GGUF runs:
Pre-fix XML leak rate: 20/900 (2.22%), concentrated 6.7% on the
larger Q8 / MTP configs:
Qwen3.6-35B-A3B Q8_0 4/60 (6.7%)
Qwen3.6-35B-A3B-MTP Q4 4/60 (6.7%)
Qwen3.5-35B-A3B Q8_0 3/60 (5.0%)
Qwen3.6-27B Q8_0 3/60 (5.0%)
The existing `_TOOL_XML_RE` only matched well-formed
`<tool_call>...</tool_call>` and `<function=...></function>` pairs, so
unterminated openings (close was DRAINED) and orphan closes (opening
was DRAINED) survived the strip and reached the user.
Fix relaxes the regex to also strip:
1. Orphan opening up to end-of-string: `(?:</tool_call>|\Z)`
2. Orphan closing tag: bare `</tool_call>` / `</function>`
Verified on the full sweep: 20/900 -> 0/900 (100% of detected leaks
eliminated). 16 unit tests in `test_tool_xml_strip.py` pin all three
leak shapes plus the well-formed cases, plus parametrised checks on
the 5 actual real-world leak samples from the sweep data.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: strip tail-only </parameter> orphan + tighten regex
The 2026-05-22 gdpval sweep surfaced a 4th XML-leak shape not caught
by the earlier regex: a bare `</parameter>\n\n` at end-of-buffer (7
of 192 trials, all Qwen3.5-27B + a few Qwen3.6-27B). The model emits
the full `<tool_call><function=...><parameter=...>...content...
</parameter></function></tool_call>` envelope, the speculative buffer
DRAINS the opening tags as intended, but EOS (max_tokens cutoff)
truncates the outer `</function></tool_call>` close, leaving just
`</parameter>` as the visible tail.
We strip this ONLY when end-anchored (`\s*\Z`) so legitimate
mid-text uses (user code samples, documentation discussing the
Qwen tool-call XML shape) survive. Verified on the 192-trial
gdpval corpus: before=7, after=0.
While at it, fold the five top-level alternations into three by
sharing tag-name and prefix subgroups:
<tool_call>... + <function=\w+>... + --> <(?:tool_call|function=\w+)>...
</tool_call> | </function> --> </(?:tool_call|function)>
Semantically identical (verified by replay over the 192-trial
corpus + adversarial inputs, 0 diffs) and 1.34x faster on real
workloads. Backtracking-safety pinned by two new perf guards
(256KB '<' spam, 1000x orphan opens).
Tests: 16 -> 28 (6 new functional + 4 well-formed-vs-orphan +
2 perf guards).
* Tighten comments in XML-strip regex and tests
Code says what it does; comments were repeating it. Strip the verbose
explanations down to the WHY-only bits (engine quirk, tail-anchor
rationale, real-world source of each test sample). No code changes.
inference.py: 21 -> 12 lines around _TOOL_XML_RE
test_tool_xml_strip.py: 343 -> 259 lines (-84)
Tests: 28/28 still pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Address review: deny pass-through --parallel, preserve legacy short aliases, fix test harness
Round 1 review fixes for #5737:
1. Deny --parallel / --n-parallel / -np in the pass-through validator.
Without this, `unsloth studio run --model X --parallel 8 -- --parallel
999` would last-win-override the running llama-server slot count while
Studio's app.state.llama_parallel_slots and KV-cache fitting stay at
the typer value (8), so the resource plan and the running process
disagree. Also bypasses the typer 1..64 range guard. Reject so the
only path is the first-class typer flag.
2. Backwards-compat shim for -m / -hfr / -f. Dropping the short aliases
from typer broke any script using `unsloth studio run -m X` or
`-hfr Y` or `-f dist`. Add _consume_legacy_short_aliases which pops
EXACT whole-token matches (or `-x=value` inline form) from ctx.args
into the corresponding typer parameter. Clustered tokens (`-fa`,
`-mg`, `-fitt`, ...) are left in the pass-through tail unchanged.
--model becomes Optional with an explicit missing-required check
after the preprocessor so legacy `-m X` still satisfies the
"must specify a model" requirement.
3. Drop mix_stderr from CliRunner. Typer 0.25.1 / Click 8.4.1 removed
the kwarg; the test harness raised TypeError before exercising the
PR behaviour. Tests run cleanly on current and older Typer/Click.
4. Correct the -np regression test docstring. Pre-PR `-np 8` was
clustered by Click as `-p 8` (port=8) + stray `-n`, silently
breaking the port binding -- not "passed through as 8 slots". The
post-PR assertion (child gets --parallel 8) is unchanged.
5. Update studio run docstring listing rejected flags so it now
correctly includes --parallel / -np / --n-parallel.
New tests:
- test_llama_server_args.py: parametrized denylist coverage for
--parallel / --n-parallel / -np including equals-form, including
out-of-range bypass attempts (999, 0). is_managed_flag flips True.
- test_studio_run_short_alias_clashes.py: legacy -m / -hfr / -f
promote to typer params; --model X + -m Y conflict errors; clustered
-mg / -fa / -fitt still pass through (the original bug fix holds).
132 tests pass (98 backend + 34 cli).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Extend legacy-alias shim tests for repo:variant, inline value form, and missing model
Three additional edge cases for the -m / -hfr / -f preprocessor:
- `-m unsloth/foo:UD-Q4_K_XL` round-trips through both the preprocessor
and _split_repo_variant so the child sees --model + --gguf-variant.
- `-m=foo` inline value form is promoted just like `-m foo`.
- Missing --model after the preprocessor raises typer.Exit(2) cleanly
(replacing typer's pre-PR required-flag enforcement now that --model
is Optional to allow the legacy promotion path).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Scrub .github/workflows for staging push (matches staging base)
* Fix studio CLI argv handling and pass-through docstring drift
- studio/backend/core/inference/llama_server_args.py: drop the stale
``-np``/``--parallel`` entry from the docstring's pass-through tunable
list. These flags moved into _DENYLIST_GROUPS so the docstring now
contradicts the validator and would mislead future maintainers
debugging the ValueError from validate_extra_args(["--parallel","8"]).
The deleted wording was introduced by
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|---|---|---|
| .github | ||
| images | ||
| scripts | ||
| studio | ||
| tests | ||
| unsloth | ||
| unsloth_cli | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
| build.sh | ||
| cli.py | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| COPYING | ||
| install.ps1 | ||
| install.sh | ||
| LICENSE | ||
| pyproject.toml | ||
| README.md | ||
| unsloth-cli.py | ||
Unsloth Studio lets you run and train models locally.
Features • Quickstart • Notebooks • Documentation
⚡ Get started
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Community:
⭐ Features
Unsloth Studio (Beta) lets you run and train text, audio, embedding, vision models on Windows, Linux and macOS.
Inference
- Search + download + run models including GGUF, LoRA adapters, safetensors
- Export models: Save or export models to GGUF, 16-bit safetensors and other formats.
- Tool calling: Support for self-healing tool calling and web search
- Code execution: lets LLMs test code in Claude artifacts and sandbox environments
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools with Unsloth
- Auto set inference settings and customize chat templates.
- We work directly with teams behind gpt-oss, Qwen3, Llama 4, Mistral, Gemma 1-3, and Phi-4, where we’ve fixed bugs that improve model accuracy.
- Chat with images, audio, PDFs, code, DOCX and more. Connect API providers (OpenAI, Anthropic) or servers (vLLM, Ollama).
Training
- Train and RL 500+ models up to 2x faster with up to 70% less VRAM, with no accuracy loss.
- Custom Triton and mathematical kernels. See some collabs we did with PyTorch and Hugging Face.
- Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
- Reinforcement Learning (RL): The most efficient RL library, using 80% less VRAM for GRPO, FP8 etc.
- Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
- Observability: Monitor training live, track loss and GPU usage and customize graphs.
- Multi-GPU training is supported, with major improvements coming soon.
📥 Install
Unsloth can be used in two ways: through Unsloth Studio, the web UI, or through Unsloth Core, the code-based version. Each has different requirements.
Unsloth Studio (web UI)
Unsloth Studio (Beta) works on Windows, Linux, WSL and macOS.
- CPU: Supported for Chat and Data Recipes currently
- NVIDIA: Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
- macOS: Training, MLX and GGUF inference are ALL supported.
- AMD: Chat + Data works. Train with Unsloth Core. Studio support is out soon.
- Multi-GPU: Available now, with a major upgrade on the way
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Launch
unsloth studio -p 8888
For cloud or global access, add -H 0.0.0.0. By default, Unsloth is accessible only locally.
Update
To update, use the same install commands above or use unsloth studio update.
Docker
Use our Docker image unsloth/unsloth container. Run:
docker run -d -e JUPYTER_PASSWORD="mypassword" \
-p 8888:8888 -p 8000:8000 -p 2222:22 \
-v $(pwd)/work:/workspace/work \
--gpus all \
unsloth/unsloth
Developer, Nightly, Uninstall
To see developer, nightly and uninstallation etc. instructions, see advanced installation.
Unsloth Core (code-based)
Linux, WSL:
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto
Windows:
winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv -e
uv venv unsloth_env --python 3.13
.\unsloth_env\Scripts\activate
uv pip install unsloth --torch-backend=auto
For Windows, pip install unsloth works only if you have PyTorch installed. Read our Windows Guide.
You can use the same Docker image as Unsloth Studio.
AMD, Intel:
For RTX 50x, B200, 6000 GPUs: uv pip install unsloth --torch-backend=auto. Read our guides for: Blackwell and DGX Spark.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.
📒 Free Notebooks
Train for free with our notebooks. You can use our new free Unsloth Studio notebook to run and train models for free in a web UI. Read our guide. Add dataset, run, then deploy your trained model.
| Model | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| Gemma 4 (E2B) | ▶️ Start for free | 1.5x faster | 50% less |
| Qwen3.5 (4B) | ▶️ Start for free | 1.5x faster | 60% less |
| gpt-oss (20B) | ▶️ Start for free | 2x faster | 70% less |
| Qwen3.5 GSPO | ▶️ Start for free | 2x faster | 70% less |
| gpt-oss (20B): GRPO | ▶️ Start for free | 2x faster | 80% less |
| Qwen3: Advanced GRPO | ▶️ Start for free | 2x faster | 70% less |
| embeddinggemma (300M) | ▶️ Start for free | 2x faster | 20% less |
| Mistral Ministral 3 (3B) | ▶️ Start for free | 1.5x faster | 60% less |
| Llama 3.1 (8B) Alpaca | ▶️ Start for free | 2x faster | 70% less |
| Llama 3.2 Conversational | ▶️ Start for free | 2x faster | 70% less |
| Orpheus-TTS (3B) | ▶️ Start for free | 1.5x faster | 50% less |
- See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
- See all our models and all our notebooks
- See detailed documentation for Unsloth here
🦥 Unsloth News
- Connections: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). Guide
- MTP: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. Guide
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools. Guide
- Qwen3.6: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. Blog
- Gemma 4: Run and train Google’s new models directly in Unsloth. Blog
- Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
- Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
- Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
- Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
- New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
- New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
- 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
- FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL
📥 Advanced Installation
The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.
Developer installs: macOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Developer installs: Windows PowerShell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Nightly: MacOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
./install.sh --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Nightly: Windows:
Run in Windows Powershell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
git checkout nightly
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh - Windows (PowerShell):
irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$HOME\.unsloth\studio" (Windows). The model cache at ~/.cache/huggingface is not touched by any of these.
For more info, see our docs.
Deleting model files
You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:
- MacOS, Linux, WSL:
~/.cache/huggingface/hub/ - Windows:
%USERPROFILE%\.cache\huggingface\hub\
💚 Community and Links
| Type | Links |
|---|---|
 Discord |
Join Discord server |
 r/unsloth Reddit |
Join Reddit community |
| 📚 Documentation & Wiki | Read Our Docs |
_logo_late_2025.svg){kind=logo} Twitter (aka X) |
Follow us on X |
| 🔮 Our Models | Unsloth Catalog |
| ✍️ Blog | Read our Blogs |
Citation
You can cite the Unsloth repo as follows:
@software{unsloth,
author = {Daniel Han, Michael Han and Unsloth team},
title = {Unsloth},
url = {https://github.com/unslothai/unsloth},
year = {2023}
}
If you trained a model with 🦥Unsloth, you can use this cool sticker!
License
Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under Apache 2.0, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license AGPL-3.0.
This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.
Thank You to
- The llama.cpp library that lets users run and save models with Unsloth
- The Hugging Face team and their libraries: transformers and TRL
- The Pytorch and Torch AO team for their contributions
- NVIDIA for their NeMo DataDesigner library and their contributions
- And of course for every single person who has contributed or has used Unsloth!