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
```

Suparious commited on Apr 16, 2024

Commit

51e746a

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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
+---
+# 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.