Text Generation
Transformers
Safetensors
mistral
nm-vllm
marlin
int4
conversational
text-generation-inference
4-bit precision
gptq
Instructions to use RedHatAI/zephyr-7b-beta-marlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Create README.md
Browse files
README.md
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---
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base_model: HuggingFaceH4/zephyr-7b-beta
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inference: true
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model_type: mistral
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quantized_by: robertgshaw2
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tags:
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- nm-vllm
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- marlin
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- int4
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---
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## TinyLlama-1.1B-Chat-v1.0
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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.
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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.
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## Inference
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Install [nm-vllm](https://github.com/neuralmagic/nm-vllm) for fast inference and low memory-usage:
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```bash
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pip install nm-vllm[sparse]
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```
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Run in a Python pipeline for local inference:
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_id = "neuralmagic/zephyr-7b-beta-marlin"
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model = LLM(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": "What is quantization in maching learning?"},
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]
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formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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sampling_params = SamplingParams(max_tokens=200)
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outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
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print(outputs[0].outputs[0].text)
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"""
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Sure! Here's a simple recipe for banana bread:
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Ingredients:
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- 3-4 ripe bananas,mashed
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- 1 large egg
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- 2 Tbsp. Flour
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- 2 tsp. Baking powder
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- 1 tsp. Baking soda
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- 1/2 tsp. Ground cinnamon
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- 1/4 tsp. Salt
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- 1/2 cup butter, melted
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- 3 Cups All-purpose flour
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- 1/2 tsp. Ground cinnamon
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Instructions:
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1. Preheat your oven to 350 F (175 C).
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"""
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```
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## Quantization
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For details on how this model was quantized and converted to marlin format, run the `quantization/apply_gptq_save_marlin.py` script:
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```bash
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pip install -r quantization/requirements.txt
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python3 quantization/apply_gptq_save_marlin.py --model-id HuggingFaceH4/zephyr-7b-beta --save-dir ./zephyr-marlin
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```
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## Slack
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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)
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