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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/zephyr-7b-beta-marlin")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/zephyr-7b-beta-marlin")
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 Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

zephyr-7b-beta-marlin / README.md

robertgshaw2

Create README.md

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	---
	base_model: HuggingFaceH4/zephyr-7b-beta
	inference: true
	model_type: mistral
	quantized_by: robertgshaw2
	tags:
	- nm-vllm
	- marlin
	- int4
	---

	## TinyLlama-1.1B-Chat-v1.0
	This repo contains model files for [zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) optimized for [nm-vllm](https://github.com/neuralmagic/nm-vllm), a high-throughput serving engine for compressed LLMs.

	This model was quantized with [GPTQ](https://arxiv.org/abs/2210.17323) and saved in the Marlin format for efficient 4-bit inference. Marlin is a highly optimized inference kernel for 4 bit models.

	## Inference
	Install [nm-vllm](https://github.com/neuralmagic/nm-vllm) for fast inference and low memory-usage:
	```bash
	pip install nm-vllm[sparse]
	```

	Run in a Python pipeline for local inference:
	```python
	from transformers import AutoTokenizer
	from vllm import LLM, SamplingParams

	model_id = "neuralmagic/zephyr-7b-beta-marlin"
	model = LLM(model_id)

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	messages = [
	{"role": "user", "content": "What is quantization in maching learning?"},
	]
	formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	sampling_params = SamplingParams(max_tokens=200)
	outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
	print(outputs[0].outputs[0].text)

	"""
	Sure! Here's a simple recipe for banana bread:

	Ingredients:
	- 3-4 ripe bananas,mashed
	- 1 large egg
	- 2 Tbsp. Flour
	- 2 tsp. Baking powder
	- 1 tsp. Baking soda
	- 1/2 tsp. Ground cinnamon
	- 1/4 tsp. Salt
	- 1/2 cup butter, melted
	- 3 Cups All-purpose flour
	- 1/2 tsp. Ground cinnamon

	Instructions:

	1. Preheat your oven to 350 F (175 C).
	"""
	```

	## Quantization
	For details on how this model was quantized and converted to marlin format, run the `quantization/apply_gptq_save_marlin.py` script:

	```bash
	pip install -r quantization/requirements.txt
	python3 quantization/apply_gptq_save_marlin.py --model-id HuggingFaceH4/zephyr-7b-beta --save-dir ./zephyr-marlin
	```

	## Slack

	For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)