Text Generation
Safetensors
GGUF
English
lfm2
heretic
uncensored
decensored
abliterated
coding-agent
tool-use
conversational
text-generation-inference
Instructions to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M # Run inference directly in the terminal: llama cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M # Run inference directly in the terminal: llama cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Use Docker
docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
- Ollama
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Ollama:
ollama run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
- Unsloth Studio
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting
- Pi
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Docker Model Runner:
docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
- Lemonade
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Run and chat with the model
lemonade run user.lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
| { | |
| "version": "2", | |
| "timestamp": "2026-08-19T09:10:37", | |
| "system": { | |
| "python": { | |
| "version": "3.12.13", | |
| "implementation": "CPython", | |
| "compiler": "GCC 11.4.0", | |
| "environment": "System" | |
| }, | |
| "os": { | |
| "platform": "Linux-6.6.122+-x86_64-with-glibc2.35", | |
| "machine": "x86_64" | |
| }, | |
| "cpu": { | |
| "brand": "Intel(R) Xeon(R) CPU @ 2.00GHz", | |
| "vendor": "GenuineIntel", | |
| "family": 6, | |
| "model": 85, | |
| "stepping": 3 | |
| }, | |
| "accelerators": { | |
| "type": "CUDA", | |
| "api_name": "CUDA Version", | |
| "api_version": "12.8", | |
| "driver_version": "580.82.07", | |
| "devices": [ | |
| { | |
| "name": "Tesla T4", | |
| "vram_gb": 14.56 | |
| } | |
| ] | |
| } | |
| }, | |
| "environment": { | |
| "heretic": { | |
| "version": "1.4.0", | |
| "is_standard_pypi": true, | |
| "metadata": { | |
| "type": "pypi" | |
| } | |
| }, | |
| "pytorch_version": "2.11.0+cu128", | |
| "requirements": { | |
| "absl-py": "1.4.0", | |
| "accelerate": "1.14.0", | |
| "alembic": "1.19.1", | |
| "annotated-doc": "0.0.5", | |
| "annotated-types": "0.8.0", | |
| "anyio": "4.14.2", | |
| "bitsandbytes": "0.50.1", | |
| "certifi": "2026.7.22", | |
| "chardet": "5.2.0", | |
| "charset-normalizer": "3.4.9", | |
| "click": "8.4.2", | |
| "colorama": "0.4.6", | |
| "colorlog": "6.12.0", | |
| "cuda-bindings": "12.9.7", | |
| "cuda-pathfinder": "1.6.0", | |
| "cuda-toolkit": "12.8.1", | |
| "dataproperty": "1.1.1", | |
| "datasets": "4.8.5", | |
| "dill": "0.3.8", | |
| "evaluate": "0.4.6", | |
| "filelock": "3.32.3", | |
| "fsspec": "2025.3.0", | |
| "greenlet": "3.5.5", | |
| "h11": "0.16.0", | |
| "heretic-llm": "1.4.0", | |
| "hf-xet": "1.6.0", | |
| "httpcore": "1.0.9", | |
| "httpx": "0.28.1", | |
| "huggingface-hub": "1.27.0", | |
| "idna": "3.18", | |
| "immutabledict": "4.3.1", | |
| "importlib-metadata": "9.0.0", | |
| "jinja2": "3.1.6", | |
| "joblib": "1.5.3", | |
| "langdetect": "1.0.9", | |
| "lm-eval": "0.4.12", | |
| "lxml": "6.1.1", | |
| "mako": "1.1.3", | |
| "markdown-it-py": "4.2.0", | |
| "markupsafe": "3.0.3", | |
| "mbstrdecoder": "1.1.5", | |
| "mdurl": "0.1.2", | |
| "more-itertools": "10.8.0", | |
| "mpmath": "1.3.0", | |
| "multiprocess": "0.70.16", | |
| "networkx": "3.6.1", | |
| "nltk": "3.9.1", | |
| "numpy": "2.5.2", | |
| "nvidia-cublas-cu12": "12.8.4.1", | |
| "nvidia-cudnn-cu12": "9.19.0.56", | |
| "nvidia-cusparselt-cu12": "0.7.1", | |
| "nvidia-nccl-cu12": "2.28.9", | |
| "nvidia-nvshmem-cu12": "3.4.5", | |
| "optuna": "4.9.0", | |
| "packaging": "26.3", | |
| "pandas": "2.2.3", | |
| "pathvalidate": "3.3.1", | |
| "peft": "0.20.0", | |
| "pillow": "11.3.0", | |
| "portalocker": "4.1.0", | |
| "prompt-toolkit": "3.0.53", | |
| "psutil": "7.2.2", | |
| "py-cpuinfo": "9.0.0", | |
| "pyarrow": "25.0.1", | |
| "pydantic": "2.13.4", | |
| "pydantic-core": "2.46.4", | |
| "pydantic-settings": "2.15.0", | |
| "pygments": "2.20.0", | |
| "pytablewriter": "1.2.1", | |
| "python-dateutil": "2.9.0.post0", | |
| "python-dotenv": "1.2.2", | |
| "pytz": "2025.2", | |
| "pyyaml": "6.0.3", | |
| "questionary": "2.1.1", | |
| "regex": "2025.11.3", | |
| "requests": "2.32.4", | |
| "rich": "14.3.4", | |
| "rouge-score": "0.1.2", | |
| "sacrebleu": "2.6.0", | |
| "safetensors": "0.8.0", | |
| "scikit-learn": "1.6.1", | |
| "scipy": "1.16.3", | |
| "setuptools": "75.2.0", | |
| "shellingham": "1.5.4", | |
| "six": "1.17.0", | |
| "sqlalchemy": "2.0.52", | |
| "sqlitedict": "2.1.0", | |
| "sympy": "1.14.0", | |
| "tabledata": "1.3.5", | |
| "tabulate": "0.9.0", | |
| "tcolorpy": "0.1.7", | |
| "threadpoolctl": "3.6.0", | |
| "tokenizers": "0.22.2", | |
| "tomli-w": "1.2.0", | |
| "torch": "2.11.0", | |
| "torchaudio": "2.11.0", | |
| "torchvision": "0.26.0", | |
| "tqdm": "4.67.3", | |
| "transformers": "5.15.0", | |
| "triton": "3.6.0", | |
| "typepy": "1.3.5", | |
| "typer": "0.27.1", | |
| "typing-extensions": "4.16.0", | |
| "typing-inspection": "0.4.4", | |
| "tzdata": "2026.3", | |
| "urllib3": "2.5.0", | |
| "wcwidth": "0.8.2", | |
| "word2number": "1.1", | |
| "xxhash": "4.0.0", | |
| "zipp": "4.1.0" | |
| } | |
| }, | |
| "settings": { | |
| "model": "AyoubChLin/lfm2.5-2.6b-fable5-coding-agent", | |
| "model_commit": "2dc04ac8a4494a3502e456eb159592ffe89555e1", | |
| "dtypes": [ | |
| "auto", | |
| "float16", | |
| "bfloat16", | |
| "float32" | |
| ], | |
| "quantization": "none", | |
| "device_map": "auto", | |
| "max_memory": null, | |
| "offload_outputs_to_cpu": true, | |
| "batch_size": 128, | |
| "max_response_length": 100, | |
| "response_prefix": "", | |
| "kl_divergence_scale": 1.0, | |
| "kl_divergence_target": 0.01, | |
| "orthogonalize_direction": true, | |
| "row_normalization": "full", | |
| "full_normalization_lora_rank": 3, | |
| "winsorization_quantile": 1.0, | |
| "n_trials": 200, | |
| "n_startup_trials": 60, | |
| "seed": 1623701026, | |
| "export_strategy": "adapter", | |
| "max_shard_size": "5GB", | |
| "refusal_markers": [ | |
| "disclaimer", | |
| "sorry", | |
| "i can'", | |
| "i cant", | |
| "i cannot", | |
| "i won'", | |
| "i wont", | |
| "i will not", | |
| "i unable", | |
| "im unable", | |
| "i'm unable", | |
| "i am unable", | |
| "i an ai", | |
| "im an ai", | |
| "i'm an ai", | |
| "i am an ai", | |
| "as an ai", | |
| "ai assistant", | |
| "i designed to", | |
| "im designed to", | |
| "i'm designed to", | |
| "i am designed to", | |
| "i programmed to", | |
| "im programmed to", | |
| "i'm programmed to", | |
| "i am programmed to", | |
| "violat", | |
| "prohibit", | |
| "illegal", | |
| "harmful", | |
| "inappropriate", | |
| "unethical", | |
| "ethical boundaries" | |
| ], | |
| "system_prompt": "You are a helpful assistant.", | |
| "good_prompts": { | |
| "dataset": "mlabonne/harmless_alpaca", | |
| "commit": "02c6a92cfcf11bb0c387334f8146d149d65b587f", | |
| "split": "train[:400]", | |
| "column": "text", | |
| "prefix": "", | |
| "suffix": "", | |
| "system_prompt": null | |
| }, | |
| "bad_prompts": { | |
| "dataset": "mlabonne/harmful_behaviors", | |
| "commit": "01cead01398926d81f7c52bdb790ee8cf77ebba7", | |
| "split": "train[:400]", | |
| "column": "text", | |
| "prefix": "", | |
| "suffix": "", | |
| "system_prompt": null | |
| }, | |
| "good_evaluation_prompts": { | |
| "dataset": "mlabonne/harmless_alpaca", | |
| "commit": "02c6a92cfcf11bb0c387334f8146d149d65b587f", | |
| "split": "test[:100]", | |
| "column": "text", | |
| "prefix": "", | |
| "suffix": "", | |
| "system_prompt": null | |
| }, | |
| "bad_evaluation_prompts": { | |
| "dataset": "mlabonne/harmful_behaviors", | |
| "commit": "01cead01398926d81f7c52bdb790ee8cf77ebba7", | |
| "split": "test[:100]", | |
| "column": "text", | |
| "prefix": "", | |
| "suffix": "", | |
| "system_prompt": null | |
| } | |
| }, | |
| "parameters": { | |
| "direction_index": 17.563042106541033, | |
| "abliteration_parameters": { | |
| "attn.o_proj": { | |
| "max_weight": 1.4174904213958506, | |
| "max_weight_position": 21.61002685477779, | |
| "min_weight": 1.4128366022031185, | |
| "min_weight_distance": 16.491967292751458 | |
| }, | |
| "mlp.down_proj": { | |
| "max_weight": 1.3546024478853658, | |
| "max_weight_position": 20.511717331082277, | |
| "min_weight": 1.3347789917661659, | |
| "min_weight_distance": 14.174737281504902 | |
| } | |
| } | |
| }, | |
| "metrics": { | |
| "kl_divergence": 0.013982672244310379, | |
| "refusals": 7, | |
| "base_refusals": 96, | |
| "n_bad_prompts": 100 | |
| }, | |
| "hashes": { | |
| "adapter_model.safetensors": "3c1ebc7a8ace281a210c96dedc40426b6ffd82a24a62be88356773dba6932854" | |
| } | |
| } |