Instructions to use hfl/llama-3-chinese-8b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hfl/llama-3-chinese-8b-instruct-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 hfl/llama-3-chinese-8b-instruct-gguf:F16 # Run inference directly in the terminal: llama cli -hf hfl/llama-3-chinese-8b-instruct-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hfl/llama-3-chinese-8b-instruct-gguf:F16 # Run inference directly in the terminal: llama cli -hf hfl/llama-3-chinese-8b-instruct-gguf:F16
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 hfl/llama-3-chinese-8b-instruct-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf hfl/llama-3-chinese-8b-instruct-gguf:F16
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 hfl/llama-3-chinese-8b-instruct-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf hfl/llama-3-chinese-8b-instruct-gguf:F16
Use Docker
docker model run hf.co/hfl/llama-3-chinese-8b-instruct-gguf:F16
- LM Studio
- Jan
- Ollama
How to use hfl/llama-3-chinese-8b-instruct-gguf with Ollama:
ollama run hf.co/hfl/llama-3-chinese-8b-instruct-gguf:F16
- Unsloth Desktop
- Docker Model Runner
How to use hfl/llama-3-chinese-8b-instruct-gguf with Docker Model Runner:
docker model run hf.co/hfl/llama-3-chinese-8b-instruct-gguf:F16
- Lemonade
How to use hfl/llama-3-chinese-8b-instruct-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hfl/llama-3-chinese-8b-instruct-gguf:F16
Run and chat with the model
lemonade run user.llama-3-chinese-8b-instruct-gguf-F16
List all available models
lemonade list
- Atomic Chat
Llama-3-Chinese-8B-Instruct-GGUF
This repository contains Llama-3-Chinese-8B-Instruct-GGUF (llama.cpp/ollama/tgw, etc. compatible), which is the quantized version of Llama-3-Chinese-8B-Instruct.
Note: this is an instruction (chat) model, which can be used for conversation, QA, etc.
Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
Performance
Metric: PPL, lower is better
Note: Old models have been removed due to its inferior performance (llama.cpp has breaking changes on pre-tokenizer).
| Quant | Size | PPL (old model) | ๐๐ป PPL (new model) |
|---|---|---|---|
| Q2_K | 2.96 GB | 10.3918 +/- 0.13288 | 9.1168 +/- 0.10711 |
| Q3_K | 3.74 GB | 6.3018 +/- 0.07849 | 5.4082 +/- 0.05955 |
| Q4_0 | 4.34 GB | 6.0628 +/- 0.07501 | 5.2048 +/- 0.05725 |
| Q4_K | 4.58 GB | 5.9066 +/- 0.07419 | 5.0189 +/- 0.05520 |
| Q5_0 | 5.21 GB | 5.8562 +/- 0.07355 | 4.9803 +/- 0.05493 |
| Q5_K | 5.34 GB | 5.8062 +/- 0.07331 | 4.9195 +/- 0.05436 |
| Q6_K | 6.14 GB | 5.7757 +/- 0.07298 | 4.8966 +/- 0.05413 |
| Q8_0 | 7.95 GB | 5.7626 +/- 0.07272 | 4.8822 +/- 0.05396 |
| F16 | 14.97 GB | 5.7628 +/- 0.07275 | 4.8802 +/- 0.05392 |
Others
For full model, please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct
For LoRA-only model, please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-lora
If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
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