Instructions to use OpenNLPLab/TransNormerLLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenNLPLab/TransNormerLLM-7B with Transformers:

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

pipe = pipeline("text-generation", model="OpenNLPLab/TransNormerLLM-7B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM-7B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenNLPLab/TransNormerLLM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenNLPLab/TransNormerLLM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenNLPLab/TransNormerLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenNLPLab/TransNormerLLM-7B

SGLang

How to use OpenNLPLab/TransNormerLLM-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 "OpenNLPLab/TransNormerLLM-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": "OpenNLPLab/TransNormerLLM-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 "OpenNLPLab/TransNormerLLM-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": "OpenNLPLab/TransNormerLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenNLPLab/TransNormerLLM-7B with Docker Model Runner:
```
docker model run hf.co/OpenNLPLab/TransNormerLLM-7B
```

OpenNLPLab commited on Nov 13, 2023

Commit

ec22eda

1 Parent(s): d58788d

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -132,12 +132,14 @@ export use_triton=False
 ### Demonstration of Base Model Inference
 ```python
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
->>> tokenizer = AutoTokenizer.from_pretrained("OpenNLPLab/TransNormerLLM-1B", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM-1B", device_map="auto", trust_remote_code=True)
 >>> inputs = tokenizer('今天是美好的一天', return_tensors='pt')
->>> pred = model.generate(**inputs, max_new_tokens=2048, repetition_penalty=1.0)
 >>> print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
 ```

 ### Demonstration of Base Model Inference
+**📝Note** Kindly utilize the model employing `bfloat16` instead of `float16`.
 ```python
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> tokenizer = AutoTokenizer.from_pretrained("OpenNLPLab/TransNormerLLM-7B", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM-7B", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
 >>> inputs = tokenizer('今天是美好的一天', return_tensors='pt')
+>>> pred = model.generate(**inputs, max_new_tokens=4096, repetition_penalty=1.0)
 >>> print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
 ```