Instructions to use MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ

SGLang

How to use MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ 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 "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ" \
    --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": "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ",
		"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 "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ" \
        --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": "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ
```

zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ / README.md

MaziyarPanahi

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metadata

license: apache-2.0
tags:
  - finetuned
  - quantized
  - 4-bit
  - gptq
  - transformers
  - safetensors
  - mistral
  - text-generation
  - Safetensors
  - text-generation-inference
  - merge
  - 7b
  - mistralai/Mistral-7B-Instruct-v0.1
  - HuggingFaceH4/zephyr-7b-beta
  - pytorch
  - generated_from_trainer
  - en
  - dataset:HuggingFaceH4/ultrachat_200k
  - dataset:HuggingFaceH4/ultrafeedback_binarized
  - arxiv:2305.18290
  - arxiv:2310.16944
  - base_model:mistralai/Mistral-7B-v0.1
  - license:mit
  - model-index
  - autotrain_compatible
  - endpoints_compatible
  - has_space
  - region:us
  - license:apache-2.0
model_name: zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ
base_model: MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi

Description

MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ is a quantized (GPTQ) version of MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1

How to use

Install the necessary packages

pip install --upgrade accelerate auto-gptq transformers

Example Python code

from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import torch

model_id = "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ"

quantize_config = BaseQuantizeConfig(
        bits=4,
        group_size=128,
        desc_act=False
    )

model = AutoGPTQForCausalLM.from_quantized(
        model_id,
        use_safetensors=True,
        device="cuda:0",
        quantize_config=quantize_config)

tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.1
)

outputs = pipe("What is a large language model?")
print(outputs[0]["generated_text"])