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
```

l3utterfly commited on Nov 21, 2023

Commit

14748a0

1 Parent(s): d464f43

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@@ -27,7 +27,6 @@ User:
 Assistant:
 ```
-[<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)
 # [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)
@@ -41,3 +40,5 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 | Winogrande (5-shot)   | 75.93   |
 | GSM8K (5-shot)        | 16.83        |
 | DROP (3-shot)         | 40.01         |

 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)
 | 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)