Instructions to use Linly-AI/Chinese-LLaMA-2-13B-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Linly-AI/Chinese-LLaMA-2-13B-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Linly-AI/Chinese-LLaMA-2-13B-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf")
model = AutoModelForCausalLM.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Linly-AI/Chinese-LLaMA-2-13B-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Linly-AI/Chinese-LLaMA-2-13B-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Linly-AI/Chinese-LLaMA-2-13B-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Linly-AI/Chinese-LLaMA-2-13B-hf

SGLang

How to use Linly-AI/Chinese-LLaMA-2-13B-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Linly-AI/Chinese-LLaMA-2-13B-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Linly-AI/Chinese-LLaMA-2-13B-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Linly-AI/Chinese-LLaMA-2-13B-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Linly-AI/Chinese-LLaMA-2-13B-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Linly-AI/Chinese-LLaMA-2-13B-hf with Docker Model Runner:
```
docker model run hf.co/Linly-AI/Chinese-LLaMA-2-13B-hf
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chinese-LLaMA-2-13B

Linly-Chinese-LLaMA2 基于 LLaMA2进行中文化训练，使用课程学习方法跨语言迁移，词表针对中文重新设计，数据分布更均衡，收敛更稳定。

训练细节和benchmark指标详见 💻 Github Repo

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf", device_map="cuda:0", torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf", use_fast=False, trust_remote_code=True)
prompt = "北京有什么好玩的地方？"

prompt = f"### Instruction:{prompt.strip()}  ### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
generate_ids = model.generate(inputs.input_ids, do_sample=True, max_new_tokens=2048, top_k=10, top_p=0.85, temperature=1, repetition_penalty=1.15, eos_token_id=2, bos_token_id=1, pad_token_id=0)
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = response.lstrip(prompt)

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