Instructions to use l3utterfly/mistral-7b-v0.1-layla-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use l3utterfly/mistral-7b-v0.1-layla-v1 with Transformers:

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

pipe = pipeline("text-generation", model="l3utterfly/mistral-7b-v0.1-layla-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("l3utterfly/mistral-7b-v0.1-layla-v1")
model = AutoModelForCausalLM.from_pretrained("l3utterfly/mistral-7b-v0.1-layla-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use l3utterfly/mistral-7b-v0.1-layla-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "l3utterfly/mistral-7b-v0.1-layla-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "l3utterfly/mistral-7b-v0.1-layla-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/l3utterfly/mistral-7b-v0.1-layla-v1

SGLang

How to use l3utterfly/mistral-7b-v0.1-layla-v1 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 "l3utterfly/mistral-7b-v0.1-layla-v1" \
    --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": "l3utterfly/mistral-7b-v0.1-layla-v1",
		"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 "l3utterfly/mistral-7b-v0.1-layla-v1" \
        --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": "l3utterfly/mistral-7b-v0.1-layla-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use l3utterfly/mistral-7b-v0.1-layla-v1 with Docker Model Runner:
```
docker model run hf.co/l3utterfly/mistral-7b-v0.1-layla-v1
```

mistral-7b-v0.1-layla-v1

File size: 1,408 Bytes

---
license: apache-2.0
language:
- en
---

# Model Card

### Model Description

Mistral 7B fine-tuned using ShareGPT datasets for multi-turn conversations.

- **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:
```

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v1)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 55.05   |
| ARC (25-shot)         | 60.15          |
| HellaSwag (10-shot)   | 83.25    |
| MMLU (5-shot)         | 60.31         |
| TruthfulQA (0-shot)   | 48.9   |
| Winogrande (5-shot)   | 75.93   |
| GSM8K (5-shot)        | 16.83        |
| DROP (3-shot)         | 40.01         |

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)