Instructions to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B", device_map="auto")

llama-cpp-python

How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B",
	filename="unsloth.Q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B 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 dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
# Run inference directly in the terminal:
llama cli -hf dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
# Run inference directly in the terminal:
llama cli -hf dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_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 dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_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 dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0

Use Docker

docker model run hf.co/dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0

LM Studio
Jan
Ollama
How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with Ollama:
```
ollama run hf.co/dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
```

Unsloth Studio

How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B 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 dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B 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 dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B to start chatting

Atomic Chat new
Docker Model Runner
How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with Docker Model Runner:
```
docker model run hf.co/dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0
```

Lemonade

How to use dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dat-lequoc/fast-apply_gguf-Q8_0-v0.12-7B:Q8_0

Run and chat with the model

lemonade run user.fast-apply_gguf-Q8_0-v0.12-7B-Q8_0

List all available models

lemonade list

fast-apply_gguf-Q8_0-v0.12-7B / config.json

LE Quoc Dat

(Trained with Unsloth)

cd209aa verified almost 2 years ago

Raw

History Blame

29 Bytes

	{
	"model_type": "qwen2"
	}