Instructions to use QuantFactory/Medichat-Llama3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Medichat-Llama3-8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Medichat-Llama3-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Medichat-Llama3-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Medichat-Llama3-8B-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 QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Medichat-Llama3-8B-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 QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Medichat-Llama3-8B-GGUF: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 QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Medichat-Llama3-8B-GGUF: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 QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Medichat-Llama3-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Medichat-Llama3-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Medichat-Llama3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Medichat-Llama3-8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Medichat-Llama3-8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Medichat-Llama3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Medichat-Llama3-8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Medichat-Llama3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Medichat-Llama3-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Medichat-Llama3-8B-GGUF 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 QuantFactory/Medichat-Llama3-8B-GGUF 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 QuantFactory/Medichat-Llama3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Medichat-Llama3-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Medichat-Llama3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Medichat-Llama3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Medichat-Llama3-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Medichat-Llama3-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Medichat-Llama3-8B-GGUF / README.md

munish0838

Create README.md

c3efb8d verified about 2 years ago

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metadata

base_model: sethuiyer/Medichat-Llama3-8B
library_name: transformers
tags:
  - mergekit
  - merge
  - medical
license: other
datasets:
  - mlabonne/orpo-dpo-mix-40k
  - Open-Orca/SlimOrca-Dedup
  - jondurbin/airoboros-3.2
  - microsoft/orca-math-word-problems-200k
  - m-a-p/Code-Feedback
  - MaziyarPanahi/WizardLM_evol_instruct_V2_196k
  - ruslanmv/ai-medical-chatbot
model-index:
  - name: Medichat-Llama3-8B
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 59.13
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 82.9
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 60.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 49.65
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 78.93
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 60.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
          name: Open LLM Leaderboard
language:
  - en
pipeline_tag: text-generation

Medichat-Llama3-8B-GGUF

This is quantized version of sethuiyer/Medichat-Llama3-8B created using llama.cpp

Model Description

Built upon the powerful LLaMa-3 architecture and fine-tuned on an extensive dataset of health information, this model leverages its vast medical knowledge to offer clear, comprehensive answers.

This model is generally better for accurate and informative responses, particularly for users seeking in-depth medical advice.

The following YAML configuration was used to produce this model:


models:
  - model: Undi95/Llama-3-Unholy-8B
    parameters:
      weight: [0.25, 0.35, 0.45, 0.35, 0.25]
      density: [0.1, 0.25, 0.5, 0.25, 0.1]
  - model: Locutusque/llama-3-neural-chat-v1-8b
  - model: ruslanmv/Medical-Llama3-8B-16bit
    parameters:
      weight: [0.55, 0.45, 0.35, 0.45, 0.55]
      density: [0.1, 0.25, 0.5, 0.25, 0.1]
merge_method: dare_ties
base_model: Locutusque/llama-3-neural-chat-v1-8b
parameters:
  int8_mask: true
dtype: bfloat16

Comparision Against Dr.Samantha 7B

Subject	Medichat-Llama3-8B Accuracy (%)	Dr. Samantha Accuracy (%)
Clinical Knowledge	71.70	52.83
Medical Genetics	78.00	49.00
Human Aging	70.40	58.29
Human Sexuality	73.28	55.73
College Medicine	62.43	38.73
Anatomy	64.44	41.48
College Biology	72.22	52.08
High School Biology	77.10	53.23
Professional Medicine	63.97	38.73
Nutrition	73.86	50.33
Professional Psychology	68.95	46.57
Virology	54.22	41.57
High School Psychology	83.67	66.60
Average	70.33	48.85

The current model demonstrates a substantial improvement over the previous Dr. Samantha model in terms of subject-specific knowledge and accuracy.

Usage:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

class MedicalAssistant:
    def __init__(self, model_name="sethuiyer/Medichat-Llama3-8B", device="cuda"):
        self.device = device
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name).to(self.device)
        self.sys_message = ''' 
        You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
        provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
        '''

    def format_prompt(self, question):
        messages = [
            {"role": "system", "content": self.sys_message},
            {"role": "user", "content": question}
        ]
        prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        return prompt

    def generate_response(self, question, max_new_tokens=512):
        prompt = self.format_prompt(question)
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        with torch.no_grad():
            outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens, use_cache=True)
        answer = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
        return answer

if __name__ == "__main__":
    assistant = MedicalAssistant()
    question = '''
    Symptoms:
    Dizziness, headache, and nausea.

    What is the differential diagnosis?
    '''
    response = assistant.generate_response(question)
    print(response)

Ollama

This model is now also available on Ollama. You can use it by running the command ollama run monotykamary/medichat-llama3 in your terminal. If you have limited computing resources, check out this video to learn how to run it on a Google Colab backend.