Instructions to use zjunlp/chatcell-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjunlp/chatcell-large with Transformers:

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

pipe = pipeline("text-generation", model="zjunlp/chatcell-large")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-large")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-large", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zjunlp/chatcell-large with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zjunlp/chatcell-large

SGLang

How to use zjunlp/chatcell-large 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 "zjunlp/chatcell-large" \
    --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": "zjunlp/chatcell-large",
		"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 "zjunlp/chatcell-large" \
        --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": "zjunlp/chatcell-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zjunlp/chatcell-large with Docker Model Runner:
```
docker model run hf.co/zjunlp/chatcell-large
```

sadgaj commited on Feb 13, 2024

Commit

c54400c

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1 Parent(s): 3d14fc8

Update README.md

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@@ -108,6 +108,13 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-large")
 model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-large")
 ```

 tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-large")
 model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-large")
+input_text="Detail the 100 starting genes for a Mix, ranked by expression level: "
+# Encode the input text and generate a response with specified generation parameters
+input_ids = tokenizer(input_text,return_tensors="pt").input_ids
+output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True)
+# Decode and print the generated output text
+output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True)
+print(output_text)
 ```