Instructions to use hfl/chinese-alpaca-2-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hfl/chinese-alpaca-2-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hfl/chinese-alpaca-2-7b-gguf",
	filename="ggml-model-f16.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 hfl/chinese-alpaca-2-7b-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/chinese-alpaca-2-7b-gguf:F16
# Run inference directly in the terminal:
llama cli -hf hfl/chinese-alpaca-2-7b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hfl/chinese-alpaca-2-7b-gguf:F16
# Run inference directly in the terminal:
llama cli -hf hfl/chinese-alpaca-2-7b-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/chinese-alpaca-2-7b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf hfl/chinese-alpaca-2-7b-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/chinese-alpaca-2-7b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hfl/chinese-alpaca-2-7b-gguf:F16

Use Docker

docker model run hf.co/hfl/chinese-alpaca-2-7b-gguf:F16

LM Studio
Jan
Ollama
How to use hfl/chinese-alpaca-2-7b-gguf with Ollama:
```
ollama run hf.co/hfl/chinese-alpaca-2-7b-gguf:F16
```

Unsloth Studio

How to use hfl/chinese-alpaca-2-7b-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 hfl/chinese-alpaca-2-7b-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 hfl/chinese-alpaca-2-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hfl/chinese-alpaca-2-7b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use hfl/chinese-alpaca-2-7b-gguf with Docker Model Runner:
```
docker model run hf.co/hfl/chinese-alpaca-2-7b-gguf:F16
```

Lemonade

How to use hfl/chinese-alpaca-2-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hfl/chinese-alpaca-2-7b-gguf:F16

Run and chat with the model

lemonade run user.chinese-alpaca-2-7b-gguf-F16

List all available models

lemonade list

Chinese-Alpaca-2-7B-GGUF

This repository contains the GGUF-v3 models (llama.cpp compatible) for Chinese-Alpaca-2-7B.

Performance

Metric: PPL, lower is better

Quant	original	imatrix (`-im`)
Q2_K	10.3705 +/- 0.14109	11.7442 +/- 0.16034
Q3_K	8.8478 +/- 0.12085	8.7104 +/- 0.11925
Q4_0	8.6418 +/- 0.11876	-
Q4_K	8.3294 +/- 0.11396	8.3034 +/- 0.11391
Q5_0	8.3320 +/- 0.11411	-
Q5_K	8.2361 +/- 0.11298	8.2136 +/- 0.11281
Q6_K	8.1956 +/- 0.11259	8.1852 +/- 0.11246
Q8_0	8.1784 +/- 0.11232	-
F16	8.1799 +/- 0.11243	-

The model with -im suffix is generated with important matrix, which has generally better performance (not always though).

Others

For Hugging Face version, please see: https://huggingface.co/hfl/chinese-alpaca-2-7b

Please refer to https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/ for more details.

Downloads last month: 240

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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