Instructions to use HuggingFaceH4/zephyr-7b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/zephyr-7b-beta with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceH4/zephyr-7b-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/zephyr-7b-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/zephyr-7b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/zephyr-7b-beta

SGLang

How to use HuggingFaceH4/zephyr-7b-beta 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/zephyr-7b-beta" \
    --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": "HuggingFaceH4/zephyr-7b-beta",
		"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 "HuggingFaceH4/zephyr-7b-beta" \
        --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": "HuggingFaceH4/zephyr-7b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceH4/zephyr-7b-beta with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/zephyr-7b-beta
```

Error in fine tuning

#14

by EviIgenius - opened Nov 2, 2023

Discussion

EviIgenius

Nov 2, 2023

I used autotrain to load and fine tune the model , but I faced this error "Please specify target_modules in peft_config"
any idea how to relsove it?

ERROR train has failed due to an exception:
ERROR Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/autotrain/utils.py", line 280, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/autotrain/trainers/clm/main.py", line 226, in train
model = get_peft_model(model, peft_config)
File "/usr/local/lib/python3.10/dist-packages/peft/mapping.py", line 106, in get_peft_model
return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config, adapter_name=adapter_name)
File "/usr/local/lib/python3.10/dist-packages/peft/peft_model.py", line 889, in init
super().init(model, peft_config, adapter_name)
File "/usr/local/lib/python3.10/dist-packages/peft/peft_model.py", line 111, in init
self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
File "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py", line 274, in __init__
super().init(model, config, adapter_name)
File "/usr/local/lib/python3.10/dist-packages/peft/tuners/tuners_utils.py", line 88, in init
self.inject_adapter(self.model, adapter_name)
File "/usr/local/lib/python3.10/dist-packages/peft/tuners/tuners_utils.py", line 205, in inject_adapter
peft_config = self._prepare_adapter_config(peft_config, model_config)
File "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py", line 550, in _prepare_adapter_config
raise ValueError("Please specify target_modules in peft_config")
ValueError: Please specify target_modules in peft_config

hannahbernstein

Nov 7, 2023

Make sure to add the LoRA Target Modules to be trained --target-modules q_proj, v_proj

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