Instructions to use solidrust/WizardLM-2-7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/WizardLM-2-7B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/WizardLM-2-7B-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/WizardLM-2-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/WizardLM-2-7B-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/WizardLM-2-7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/WizardLM-2-7B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/WizardLM-2-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/WizardLM-2-7B-AWQ

SGLang

How to use solidrust/WizardLM-2-7B-AWQ 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 "solidrust/WizardLM-2-7B-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/WizardLM-2-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "solidrust/WizardLM-2-7B-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/WizardLM-2-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/WizardLM-2-7B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/WizardLM-2-7B-AWQ
```

WizardLM-2-7B-AWQ / README.md

Suparious

Update README.md

fa67a7f verified about 2 years ago

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	---
	license: apache-2.0
	tags:
	- transformers
	- safetensors
	- mistral
	- finetuned
	- 4-bit
	- AWQ
	- text-generation
	- text-generation-inference
	- autotrain_compatible
	- endpoints_compatible
	- chatml
	- arxiv:2304.12244
	- arxiv:2306.08568
	- arxiv:2308.09583
	model_creator: microsoft
	model_name: WizardLM-2-7B
	base_model: microsoft/WizardLM-2-7B
	inference: false
	pipeline_tag: text-generation
	quantized_by: Suparious
	---
	# microsoft/WizardLM-2-7B AWQ

	- Model creator: [microsoft](https://huggingface.co/microsoft)
	- Original model: [WizardLM-2-7B](https://huggingface.co/microsoft/WizardLM-2-7B)

	## Model Summary

	We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models,
	which have improved performance on complex chat, multilingual, reasoning and agent.
	New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.

	## How to use

	### Install the necessary packages

	```bash
	pip install --upgrade accelerate autoawq autoawq-kernels transformers
	```

	### Example Python code

	```python
	from awq import AutoAWQForCausalLM
	from transformers import AutoTokenizer, TextStreamer

	model_path = "solidrust/WizardLM-2-7B-AWQ"
	system_message = "You are WizardLM, incarnated as a powerful AI."

	# Load model
	model = AutoAWQForCausalLM.from_quantized(model_path,
	fuse_layers=True)
	tokenizer = AutoTokenizer.from_pretrained(model_path,
	trust_remote_code=True)
	streamer = TextStreamer(tokenizer,
	skip_prompt=True,
	skip_special_tokens=True)

	# Convert prompt to tokens
	prompt_template = """\
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant"""

	prompt = "You're standing on the surface of the Earth. "\
	"You walk one mile south, one mile west and one mile north. "\
	"You end up exactly where you started. Where are you?"

	tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
	return_tensors='pt').input_ids.cuda()

	# Generate output
	generation_output = model.generate(tokens,
	streamer=streamer,
	max_new_tokens=512)

	```

	### About AWQ

	AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

	AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

	It is supported by:

	- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
	- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
	- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
	- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
	- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

	## Prompt template: ChatML

	```plaintext
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	```