Instructions to use solidrust/Mistral-22B-v0.1-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Mistral-22B-v0.1-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/Mistral-22B-v0.1-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Mistral-22B-v0.1-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Mistral-22B-v0.1-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/Mistral-22B-v0.1-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Mistral-22B-v0.1-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/Mistral-22B-v0.1-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/Mistral-22B-v0.1-AWQ

SGLang

How to use solidrust/Mistral-22B-v0.1-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/Mistral-22B-v0.1-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/Mistral-22B-v0.1-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/Mistral-22B-v0.1-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/Mistral-22B-v0.1-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/Mistral-22B-v0.1-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Mistral-22B-v0.1-AWQ
```

Vezora/Mistral-22B-v0.1 AWQ

Model creator: Vezora
Original model: Mistral-22B-v0.1

Model Summary

This model is not an moe, it is infact a 22B parameter dense model!

Just one day after the release of Mixtral-8x-22b, we are excited to introduce our handcrafted experimental model, Mistral-22b-V.01. This model is a culmination of equal knowledge distilled from all experts into a single, dense 22b model. This model is not a single trained expert, rather its a compressed MOE model, turning it into a dense 22b mode. This is the first working MOE to Dense model conversion.

How to use

GUANACO PROMPT FORMAT YOU MUST USE THE GUANACO PROMPT FORMAT SHOWN BELOW. Not using this prompt format will lead to sub optimal results.

This model requires a specific chat template, as the training format was Guanaco this is what it looks like:
"### System: You are a helpful assistant. ### Human###: Give me the best chili recipe you can ###Assistant: Here is the best chili recipe..."