Instructions to use HuggingFaceH4/starchat-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/starchat-alpha with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/starchat-alpha")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/starchat-alpha")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceH4/starchat-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/starchat-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/starchat-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/starchat-alpha

SGLang

How to use HuggingFaceH4/starchat-alpha 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 "HuggingFaceH4/starchat-alpha" \
    --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": "HuggingFaceH4/starchat-alpha",
		"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 "HuggingFaceH4/starchat-alpha" \
        --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": "HuggingFaceH4/starchat-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceH4/starchat-alpha with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/starchat-alpha
```

max new tokens error despite setting lower size, The size of tensor a (8192) must match the size of tensor b (8193) at non-singleton dimension 2

#12

by LaZy3138 - opened May 29, 2023

Discussion

LaZy3138

May 29, 2023

code:

inputs = str.format("<|system|>\n<|end|>\n<|user|>%s<|end|>\n<|assistant|>" % (question))
logger.info(f"input is :\n{inputs}.")
pipe = pipeline("text-generation", model="/root_fs/home/tonyaw/machine_learning/nlp/huggingface.co/starchat-alpha")
outputs = pipe(inputs, max_new_tokens=8000)
logger.info(f"output=\n{outputs}")

error:

Unhandled Exception
Traceback (most recent call last):
File "./llm_test.py", line 126, in
    starchat_test()
File "./llm_test.py", line 117, in starchat_test
    outputs = pipe(inputs, max_new_tokens=8000)
File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/text_generation.py", line 209, in call
    return super().call(text_inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py", line 1109, in call
    return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py", line 1116, in run_single
    model_outputs = self.forward(model_inputs, **forward_params)
File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py", line 1015, in forward
    model_outputs = self._forward(model_inputs, **forward_params)
File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/text_generation.py", line 251, in _forward
    generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py", line 1437, in generate
    return self.greedy_search(
File "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py", line 2248, in greedy_search
    outputs = self(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py", line 808, in forward
    transformer_outputs = self.transformer(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py", line 605, in forward
    self_attention_mask = self_attention_mask * attention_mask.view(batch_size, 1, -1).to(
RuntimeError: The size of tensor a (8192) must match the size of tensor b (8193) at non-singleton dimension 2

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