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

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

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"

saidutta69 commited on 3 days ago

Commit

2ba22e2

verified ·

1 Parent(s): b636a20

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+# Reproduction guide
+This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.
+## Models
+- **Base model:** [AyoubChLin/lfm2.5-2.6b-fable5-coding-agent](https://huggingface.co/AyoubChLin/lfm2.5-2.6b-fable5-coding-agent) (Commit: [`2dc04ac`](https://huggingface.co/AyoubChLin/lfm2.5-2.6b-fable5-coding-agent/commit/2dc04ac8a4494a3502e456eb159592ffe89555e1))
+## Datasets
+- **Good prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
+- **Bad prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
+- **Good evaluation prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
+- **Bad evaluation prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
+## Selected trial
+- **Trial number:** 192
+- **KL divergence:** 0.013983
+- **Refusals:** 7/100
+## System
+- **Python:** 3.12.13 (CPython, GCC 11.4.0) [System]
+- **Operating system:** Linux-6.6.122+-x86_64-with-glibc2.35 (x86_64)
+- **CPU:** Intel(R) Xeon(R) CPU @ 2.00GHz
+### Accelerators
+- **CUDA:** Detected 1 device(s) (14.56 GB total VRAM)
+  - **CUDA Version:** 12.8
+  - **Driver Version:** 580.82.07
+- **Devices:**
+  - **CUDA 0:** Tesla T4 (14.56 GB)
+## Environment
+- **Heretic:** v1.4.0 (Origin: PyPI)
+- **PyTorch:** 2.11.0+cu128
+- **Other dependencies:** See [`requirements.txt`](requirements.txt).
+## Contents of this directory
+- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
+- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
+- [`AyoubChLin--lfm2--5-2--6b-fable5-coding-agent.jsonl`](AyoubChLin--lfm2--5-2--6b-fable5-coding-agent.jsonl): The Optuna study journal containing the history of all trials.
+- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
+- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
+## How to reproduce
+> [!TIP]
+> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
+> `heretic --reproduce reproduce.json`.
+1. Ensure your system matches the specifications in the **System** section above. Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.
+1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
+1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
+1. Install the correct version of PyTorch: `pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128`
+1. Place the provided `config.toml` in your working directory.
+1. Run Heretic without any additional arguments: `heretic`
+1. Wait for the run to finish, then select trial **192** and export the model.
+1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`:
+   `sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
+> [!TIP]
+> To use the included Optuna study journal `AyoubChLin--lfm2--5-2--6b-fable5-coding-agent.jsonl`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
+>
+> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.