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", device_map="auto")
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 Settings

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
```

edbeeching HF Staff commited on Oct 26, 2023

Commit

63bc8ee

1 Parent(s): 8bf7bff

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@@ -37,6 +37,28 @@ Zephyr is a series of language models that are trained to act as helpful assista
 - **Repository:** https://github.com/huggingface/alignment-handbook
 - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
 ## Intended uses & limitations
 The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.

 - **Repository:** https://github.com/huggingface/alignment-handbook
 - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
+## Performance
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png)
+| Model | Size | Align | MT-Bench (score) | AlpacaEval (win %) |
+|-------------|-----|----|---------------|--------------|
+| StableLM-Tuned-α | 7B| dSFT |2.75| -|
+| MPT-Chat |  7B |dSFT |5.42| -|
+| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
+| Mistral-Instructv0.1 | 7B|  - | 6.84 |-|
+| Zephyr-7b-α |7B|  dDPO| 6.88| -|
+| **Zephyr-7b-β** |7B|  dDPO| 7.34| 90.60|
+| Falcon-Instruct |  40B |dSFT |5.17 |45.71|
+| Guanaco 65B |  SFT |6.41| 71.80|
+| Llama2-Chat |  70B |RLHF |6.86| 92.66|
+| Vicuna v1.3 |  33B |dSFT |7.12 |88.99|
+| WizardLM v1.0 |  70B |dSFT |7.71 |-|
+| Xwin-LM v0.1 |   70B |dPPO |- |95.57|
+| GPT-3.5-turbo | - |RLHF |7.94 |89.37|
+| Claude 2 |  - |RLHF |8.06| 91.36|
+| GPT-4 |  -| RLHF |8.99| 95.28|
 ## Intended uses & limitations
 The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.