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.