Instructions to use ByteWave/Cheus-11B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteWave/Cheus-11B with Transformers:

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

pipe = pipeline("text-generation", model="ByteWave/Cheus-11B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteWave/Cheus-11B")
model = AutoModelForCausalLM.from_pretrained("ByteWave/Cheus-11B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ByteWave/Cheus-11B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteWave/Cheus-11B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteWave/Cheus-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ByteWave/Cheus-11B

SGLang

How to use ByteWave/Cheus-11B 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 "ByteWave/Cheus-11B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteWave/Cheus-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ByteWave/Cheus-11B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteWave/Cheus-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ByteWave/Cheus-11B with Docker Model Runner:
```
docker model run hf.co/ByteWave/Cheus-11B
```

Can't load model due to tensor shape mismatch

by TheBloke - opened Nov 19, 2023

Discussion

TheBloke

Nov 19, 2023

•

edited Nov 19, 2023

Hi, thanks for the model!

I'm trying to load it, and am getting this:

ValueError: Trying to set a tensor of shape torch.Size([32000, 4096]) in "weight" (which has shape torch.Size([32002, 4096])), this look incorrect.

It seems to be a mismatch between the base vocab size of 32000, and the size with the extra two tokens of 32002.

I tried some simple JSON file edits, but haven't managed to resolve it that way.

Did you manage to load it yourself, and if so what is required to do so?

Thanks

Demonstration of issue:

>>> from transformers import AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("ByteWave/Cheus-11B", low_cpu_mem_usage=True)
Loading checkpoint shards:   0%|                                                                                                     | 0/3 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 566, in from_pretrained
    return model_class.from_pretrained(
  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3480, in from_pretrained
    ) = cls._load_pretrained_model(
  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3870, in _load_pretrained_model
    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/modeling_utils.py", line 743, in _load_state_dict_into_meta_model
    set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 285, in set_module_tensor_to_device
    raise ValueError(
ValueError: Trying to set a tensor of shape torch.Size([32000, 4096]) in "weight" (which has shape torch.Size([32002, 4096])), this look incorrect.
>>>

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