Not-For-All-Audiences

nsfw

AWQ

chatml

text-generation-inference

awq

Model card Files Files and versions

xet

Community

Instructions to use solidrust/LewdMistral-7B-0.2-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use solidrust/LewdMistral-7B-0.2-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/LewdMistral-7B-0.2-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/LewdMistral-7B-0.2-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/LewdMistral-7B-0.2-AWQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use solidrust/LewdMistral-7B-0.2-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/LewdMistral-7B-0.2-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/LewdMistral-7B-0.2-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/LewdMistral-7B-0.2-AWQ

SGLang

How to use solidrust/LewdMistral-7B-0.2-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/LewdMistral-7B-0.2-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/LewdMistral-7B-0.2-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/LewdMistral-7B-0.2-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/LewdMistral-7B-0.2-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/LewdMistral-7B-0.2-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/LewdMistral-7B-0.2-AWQ
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

Undi95/LewdMistral-7B-0.2 AWQ

Model creator: Undi95
Original model: LewdMistral-7B-0.2

model Summary

It's a full finetune (on 2 epoch) of Mistral-7B-v0.2 based on multiple RP datasets.

It was made for being merged with old 0.1 model as an experiment to see if it would be possible to add new data from 0.2 into 0.1 finetunes, but since it's usable, I let is open for further train/merging.

It was used to create BigL, a model who take Mistral 0.2 7B as a base, but merged with Mistral 0.1 finetunes.

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{system prompt}

### Input:
{prompt}

### Response:
{output}