Instructions to use tinyopsec/llama-3-chinese-8b-instruct-v3-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 tinyopsec/llama-3-chinese-8b-instruct-v3-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 tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Ollama
How to use tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-chinese-8b-instruct-v3-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

llama-3-chinese-8b-instruct-v3-GGUF

GGUF quantizations of hfl/llama-3-chinese-8b-instruct-v3.

Quantizations

File	Bits	Size	Use Case
model_f16.gguf	16	~16.1 GB	Full precision, best quality
model_q8_0.gguf	8	~8.5 GB	Near-lossless, recommended if VRAM allows
model_q6_k.gguf	6	~6.6 GB	High quality, small size reduction
model_q5_k_m.gguf	5	~5.7 GB	Balanced quality/size
model_q5_k_s.gguf	5	~5.5 GB	Slightly smaller than Q5_K_M
model_q4_k_m.gguf	4	~4.9 GB	Good quality, widely used
model_q4_k_s.gguf	4	~4.6 GB	Smaller Q4, minor quality drop
model_q3_k_l.gguf	3	~4.0 GB	Low quality, use if memory-constrained
model_q3_k_m.gguf	3	~3.7 GB	Lower quality
model_q3_k_s.gguf	3	~3.5 GB	Minimum Q3
model_q2_k.gguf	2	~3.0 GB	Lowest quality, extreme compression

VRAM Requirements

Quantization	VRAM
F16	~16 GB
Q8_0	~9 GB
Q6_K	~7 GB
Q5_K_M	~6 GB
Q4_K_M	~5 GB
Q3_K_M	~4 GB
Q2_K	~3.5 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "You are a helpful assistant." -cnv

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=8192)
output = llm("你好，请介绍一下自己。", max_tokens=512)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF

Original Model

Base: hfl/llama-3-chinese-8b-instruct-v3
Developer: Joint Laboratory of HIT and iFLYTEK Research (HFL)
Architecture: LlamaForCausalLM
Parameters: 8B
Context length: 8192 tokens
License: Apache 2.0

Description

Llama-3-Chinese-8B-Instruct-v3 is an instruction-tuned model fine-tuned from Meta-Llama-3-8B-Instruct, further enhanced through iterative training on Chinese-specific data (building on v1 and v2). Optimized for Chinese conversational AI, instruction-following, and question answering tasks in both Chinese and English.

Downloads last month: 1,179

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

hfl/llama-3-chinese-8b-instruct-v3

Quantized

(5)

this model

Collection including tinyopsec/llama-3-chinese-8b-instruct-v3-GGUF

📦 GGUF Quants

Collection

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