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 Settings

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
```

Mistral-22B-v0.1-AWQ / README.md

Suparious

Updated base_model tag in README.md

9b938ab verified almost 2 years ago

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	---
	tags:
	- quantized
	- 4-bit
	- AWQ
	- autotrain_compatible
	- endpoints_compatible
	- text-generation-inference
	license: apache-2.0
	language:
	- en
	base_model: Vezora/Mistral-22B-v0.1
	model_creator: Vezora
	model_name: Mistral-22B-v0.1
	model_type: mistral
	pipeline_tag: text-generation
	inference: false
	---
	# Vezora/Mistral-22B-v0.1 AWQ

	- Model creator: [Vezora](https://huggingface.co/Vezora)
	- Original model: [Mistral-22B-v0.1](https://huggingface.co/Vezora/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..."