Instructions to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alpha-ai/LLAMA3-3B-Medical-COT-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF 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 alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Ollama:
```
ollama run hf.co/alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M
```
Unsloth Desktop

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Docker Model Runner:
```
docker model run hf.co/alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M
```

Lemonade

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LLAMA3-3B-Medical-COT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF: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 alpha-ai/LLAMA3-3B-Medical-COT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alpha-ai/LLAMA3-3B-Medical-COT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/LLAMA3-3B-Medical-COT-GGUF: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 "alpha-ai/LLAMA3-3B-Medical-COT-GGUF: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"

Website - https://www.alphaai.biz

Uploaded Model - LLAMA3-3B-Medical-COT

Developed by: Alpha AI
License: Apache-2.0
Fine-tuned from model: LLAMA-3.2-3B-Instruct
This LLAMA-3.2-3B-Instruct model was fine-tuned using Unsloth and Hugging Face’s TRL library, ensuring efficient training and high-quality inference.

Overview

LLAMA3-3B-Medical-COT is a fine-tuned reasoning and medical problem-solving model built over LLAMA-3.2-3B-Instruct. The model is trained on a dataset focused on open-ended medical problems, aimed at enhancing clinical reasoning and structured problem-solving in AI systems.

This dataset consists of challenging medical exam-style questions with verifiable answers, ensuring factual consistency in responses. The fine-tuning process has strengthened the model’s chain-of-thought (CoT) reasoning, allowing it to break down complex medical queries step by step while maintaining conversational fluency.

Designed for on-device and local inference, the model is optimized for quick and structured reasoning, making it highly efficient for healthcare applications, academic research, and AI-driven medical support tools.

Model Details

Model: LLAMA-3.2-3B-Instruct
Fine-tuned By: Alpha AI
Training Framework: Unsloth + Hugging Face TRL
License: Apache-2.0
Format: GGUF (Optimized for local use)

Quantization Levels Available:

q4_k_m
q5_k_m
q8_0
16-bit Precision (https://huggingface.co/alphaaico/LLAMA3-3B-Medical-COT)

Use Cases:

Medical Reasoning & Diagnosis Support – Assists in clinical discussions, case reviews, and problem-solving for medical professionals.
AI-Assisted Medical Learning – Enhances student learning through structured explanations and reasoning on medical exam questions.
Logical & Step-by-Step Problem Solving – Handles structured inference tasks beyond medical reasoning, making it useful in scientific research.
Conversational AI for Healthcare – Powers virtual assistants and AI-driven consultation tools with evidence-based responses.

Model Performance:

Fine-tuned on Verified Medical Reasoning Data – Ensures step-by-step logical responses grounded in medical accuracy.
Optimized for Local Deployment – Runs efficiently on personal GPUs and edge devices without requiring cloud infrastructure.
Structured Thought Process – Breaks down complex medical questions into logical, evidence-based answers.

Limitations & Biases:

While trained on verified medical datasets, this model is not a replacement for professional medical advice and should be used as a supplementary tool rather than a definitive diagnostic system. The model may exhibit biases from its dataset, and responses should always be validated by medical experts before being used in real-world applications.

Acknowledgments Special thanks to:

Unsloth for optimizing fine-tuning pipelines.
Hugging Face TRL for robust model training tools.
Dataset contributors for providing structured medical reasoning problems.

Downloads last month: 350

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Collection including alpha-ai/LLAMA3-3B-Medical-COT-GGUF

Finetunes | SLMs and LLMs

Collection

Various variants of LLMs finetuned using proprietary data. • 28 items • Updated Sep 11 • 4