Instructions to use Linly-AI/Chinese-Falcon-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linly-AI/Chinese-Falcon-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Linly-AI/Chinese-Falcon-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Linly-AI/Chinese-Falcon-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Linly-AI/Chinese-Falcon-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Linly-AI/Chinese-Falcon-7B" # 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-Falcon-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Linly-AI/Chinese-Falcon-7B
- SGLang
How to use Linly-AI/Chinese-Falcon-7B 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-Falcon-7B" \ --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-Falcon-7B", "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-Falcon-7B" \ --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-Falcon-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Linly-AI/Chinese-Falcon-7B with Docker Model Runner:
docker model run hf.co/Linly-AI/Chinese-Falcon-7B
Training details: https://github.com/CVI-SZU/Linly
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "Linly-AI/Chinese-Falcon-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"User: 你如何看待996?\nBot: 我认为996制度是一种不可取的工作时间安排,因为这会导致员工过多的劳累和身心健康问题。此外,如果公司想要提高生产效率,应该采用更有效的管理方式,而不是通过强行加大工作量来达到目的。\nUser: 那么你有什么建议?\nBot:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
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