Instructions to use jg007/GaMS3-12B-Instruct-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jg007/GaMS3-12B-Instruct-Q6_K-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 jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

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 jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

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 jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use jg007/GaMS3-12B-Instruct-Q6_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jg007/GaMS3-12B-Instruct-Q6_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jg007/GaMS3-12B-Instruct-Q6_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

Ollama
How to use jg007/GaMS3-12B-Instruct-Q6_K-GGUF with Ollama:
```
ollama run hf.co/jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
```
Unsloth Desktop
Docker Model Runner
How to use jg007/GaMS3-12B-Instruct-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K
```

Lemonade

How to use jg007/GaMS3-12B-Instruct-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jg007/GaMS3-12B-Instruct-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.GaMS3-12B-Instruct-Q6_K-GGUF-Q6_K

List all available models

lemonade list

Atomic Chat

GaMS3-12B-Instruct-Q6_K-GGUF / gams3-12b-instruct-q6_k.gguf

jg007

Upload gams3-12b-instruct-q6_k.gguf with huggingface_hub

a6828c6 verified 6 months ago

Download

History Blame GGUF Editor new Delete

9.66 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:: 51e553fdc241a27b14557f1d26f08bc4eb03771409f15f6a39a42a9f2f274bd8
Size of remote file:: 9.66 GB
SHA256:: 05f6ad965f46727ea0f4961901d6e9fd5773398d17e8ca90dc14f0353bb0e8c9

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