Instructions to use mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw")
model = AutoModelForCausalLM.from_pretrained("mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw

SGLang

How to use mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw 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 "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw" \
    --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": "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw",
		"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 "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw" \
        --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": "mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw with Docker Model Runner:
```
docker model run hf.co/mpasila/mistral-7b-v0.1-layla-v4-exl2-4bpw
```

This is an ExLlamaV2 quantized model in 4bpw of l3utterfly/mistral-7b-v0.1-layla-v4 using the default calibration dataset.

Original Model card:

Model Card

Model Description

Mistral 7B fine-tuned by the OpenHermes 2.5 dataset optimised for multi-turn conversation and character impersonation.

The dataset has been pre-processed by doing the following:

remove all refusals
remove any mention of AI assistant
split any multi-turn dialog generated in the dataset into multi-turn conversations records
added nfsw generated conversations from the Teatime dataset

Developed by: l3utterfly
Funded by: Layla Network
Model type: Mistral
Language(s) (NLP): English
License: Apache-2.0
Finetuned from model: Mistral 7B

Uses

Base model used by Layla - the offline personal assistant: https://www.layla-network.ai

Help & support: https://discord.gg/x546YJ6nYC

Prompt:

USER:
ASSISTANT:

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All my EXL2 quants here. • 32 items • Updated May 18, 2024