Instructions to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled 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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
# Run inference directly in the terminal:
llama cli -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
# Run inference directly in the terminal:
llama cli -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

Use Docker

docker model run hf.co/mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

LM Studio
Jan
Ollama
How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Ollama:
```
ollama run hf.co/mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
```

Unsloth Studio

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled 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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled 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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled to start chatting

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

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": "mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Docker Model Runner:
```
docker model run hf.co/mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0
```

Lemonade

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

Run and chat with the model

lemonade run user.Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled-Q4_0

List all available models

lemonade list

Hermes Agent

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

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 mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0

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 "mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled:Q4_0" \
  --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"

Uploaded Model

Overview

This model is a Llama 3.2 3B Instruct variant that has been specifically fine-tuned using reasoning data from Claude 3.7 Sonnet. The goal was to leverage Claude's renowned reasoning capabilities within a more accessible, open-source architecture like Llama.

Technical Details

Developed by: mfielding92
Base Model: unsloth/llama-3.2-3b-instruct
Finetuning Method: Supervised Fine-Tuning (SFT) using LoRA
Training Speed Enhancement: Trained 2x faster with Unsloth and Huggingface's TRL library

Training Data

The model was fine-tuned on a dataset derived from:

mfielding92/claude-3.7-sonnet-reasoning

This allows the model to potentially exhibit improved logical thinking, problem-solving abilities, and complex reasoning compared to the base Llama 3.2 model while remaining open-source.

Usage Notes

While this model inherits some of Claude's strenghs in reasoning, it is still a derivative work built on Llama architecture. Users should evaluate its performance carefully for their specific use cases.

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 735

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

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mfielding92
/

Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled

Uploaded Model

Overview

Technical Details

Training Data

Usage Notes

Dataset used to train mfielding92/Llama-3.2-3B-claude-3.7-sonnet-reasoning-distilled