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

Libraries

How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed with Pruna AI:

from pruna import PrunaModel
model = PrunaModel.from_pretrained("PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed 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 PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed: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 PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed: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 PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M

Use Docker

docker model run hf.co/PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M

LM Studio
Jan
Ollama
How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed with Ollama:
```
ollama run hf.co/PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
```

Unsloth Studio

How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed 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 PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed 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 PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed with Docker Model Runner:
```
docker model run hf.co/PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M
```

Lemonade

How to use PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PrunaAI/Llama3-8B-Chinese-Chat-GGUF-smashed:Q4_K_M

Run and chat with the model

lemonade run user.Llama3-8B-Chinese-Chat-GGUF-smashed-Q4_K_M

List all available models

lemonade list

Llama3-8B-Chinese-Chat-GGUF-smashed / Llama3-8B-Chinese-Chat.IQ3_S.gguf

johnrachwanpruna

Upload Llama3-8B-Chinese-Chat.IQ3_S.gguf with huggingface_hub

4792c3f verified over 2 years ago

Download

History Blame GGUF Editor new Delete

3.68 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:: 6182f42db95e2143287c5d77d5fd81318e241e5f2be138765268c9bb8ed326b5
Size of remote file:: 3.68 GB
SHA256:: f00c7f7618e36bdcd851d59338573ff604c58a791d547cc17763a3ed94b03d23

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