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
```

[AUTOMATED] Model Memory Requirements

#35

by model-sizer-bot - opened Nov 24, 2023

Discussion

model-sizer-bot

Nov 24, 2023

Model Memory Requirements

You will need about {'dtype': 'float16/bfloat16', 'Largest Layer or Residual Group': '432.02 MB', 'Total Size': '13.74 GB', 'Training using Adam': '54.98 GB'} VRAM to load this model for inference, and {'dtype': 'int4', 'Largest Layer or Residual Group': '108.0 MB', 'Total Size': '3.44 GB', 'Training using Adam': '13.74 GB'} VRAM to train it using Adam.

These calculations were measured from the Model Memory Utility Space on the Hub.

The minimum recommended vRAM needed for this model assumes using Accelerate or device_map="auto" and is denoted by the size of the "largest layer".
When performing inference, expect to add up to an additional 20% to this, as found by EleutherAI. More tests will be performed in the future to get a more accurate benchmark for each model.

When training with Adam, you can expect roughly 4x the reported results to be used. (1x for the model, 1x for the gradients, and 2x for the optimizer).

Results:

dtype	Largest Layer or Residual Group	Total Size	Training using Adam
float32	864.03 MB	27.49 GB	109.96 GB
float16/bfloat16	432.02 MB	13.74 GB	54.98 GB
int8	216.01 MB	6.87 GB	27.49 GB
int4	108.0 MB	3.44 GB	13.74 GB

JohnCHENMEL

Nov 28, 2023

model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-alpha", trust_remote_code=True, torch_dtype=torch.int8)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha", trust_remote_code=True, torch_dtype=torch.int8)
When I change float16 to int8, the model can not run ,do you know why?

muellerzr

Nov 28, 2023

Define can not run? Can we get a full trace/what it states for you?

JohnCHENMEL

Nov 29, 2023

When I run the following code on colab:

!pip install transformers
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True, torch_dtype=torch.float16)
inputs = tokenizer("do you know the difference btween meter and metre?", return_tensors="pt", return_attention_mask=True)
outputs = model.generate(**inputs, max_length=100, num_beams=1, num_return_sequences=1)
text = tokenizer.batch_decode(outputs)[0]
print(text)
torch.cuda.empty_cache()

system said:
OutOfMemoryError: CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 14.75 GiB of which 30.81 MiB is free. Process 2098 has 14.71 GiB memory in use. Of the allocated memory 14.45 GiB is allocated by PyTorch, and 153.47 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

I have two questions:
1,20days ago,I can run on colab properly,but now it show outof memoryerror;
2,when I change torch.float16 to torch.int8, sysytem said:
ValueError: Can't instantiate MistralForCausalLM model under dtype=torch.int8 since it is not a floating point dtype

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