Instructions to use hanzohazashi1/medical_qna-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hanzohazashi1/medical_qna-gguf with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hanzohazashi1/medical_qna-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hanzohazashi1/medical_qna-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 hanzohazashi1/medical_qna-gguf
# Run inference directly in the terminal:
llama cli -hf hanzohazashi1/medical_qna-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hanzohazashi1/medical_qna-gguf
# Run inference directly in the terminal:
llama cli -hf hanzohazashi1/medical_qna-gguf

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 hanzohazashi1/medical_qna-gguf
# Run inference directly in the terminal:
./llama-cli -hf hanzohazashi1/medical_qna-gguf

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 hanzohazashi1/medical_qna-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hanzohazashi1/medical_qna-gguf

Use Docker

docker model run hf.co/hanzohazashi1/medical_qna-gguf

LM Studio
Jan
Ollama
How to use hanzohazashi1/medical_qna-gguf with Ollama:
```
ollama run hf.co/hanzohazashi1/medical_qna-gguf
```

Unsloth Studio

How to use hanzohazashi1/medical_qna-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 hanzohazashi1/medical_qna-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 hanzohazashi1/medical_qna-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hanzohazashi1/medical_qna-gguf to start chatting

Docker Model Runner
How to use hanzohazashi1/medical_qna-gguf with Docker Model Runner:
```
docker model run hf.co/hanzohazashi1/medical_qna-gguf
```

Lemonade

How to use hanzohazashi1/medical_qna-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hanzohazashi1/medical_qna-gguf

Run and chat with the model

lemonade run user.medical_qna-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

medical_qna-gguf / base_model_1.gguf

hanzohazashi1

Upload base_model_1.gguf with huggingface_hub

86e48f8 verified about 2 years ago

Download

History Blame GGUF Editor new Delete

16.1 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: acd7a5102a840c4114e00022cf8c03a8dda1e7292f0b63e27bca942e26766059
Size of remote file:: 16.1 GB
SHA256:: b11dc030060201f65a9241e911b61961a336b88535f07faf0d69ddcbf41d306d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.