Instructions to use EmbeddedLLM/Mistral-7B-Merge-14-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="EmbeddedLLM/Mistral-7B-Merge-14-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.2")
model = AutoModelForCausalLM.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmbeddedLLM/Mistral-7B-Merge-14-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2

SGLang

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.2 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 "EmbeddedLLM/Mistral-7B-Merge-14-v0.2" \
    --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": "EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
		"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 "EmbeddedLLM/Mistral-7B-Merge-14-v0.2" \
        --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": "EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.2 with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2
```

no commercial usability

by rezzie-rich - opened Dec 19, 2023

Discussion

rezzie-rich

Dec 19, 2023

This is a brilliant model so it's sadder to see how a single model out of 14 can cripple a model from having any real-world application commercially. Though 99%+ models in this merge are under either Apache 2.0 or MIT licence, the starling model from Berkeley is under cc-by-nc-4.0 licence which poisons the rest and makes the final model not commercially usable even though it's under Apache 2.0 licence. It is very annoying since even the starling model itself is just a fine-tuned version of a real open-source model under Apache 2.0

pstan

EmbeddedLLM org Dec 19, 2023

I understand your concerns and appreciate your feedback. We are currently working on this issue and will rectify it as soon as possible. We plan to reupload another model that aligns with the commercial usability requirements. Thank you for bringing this to our attention.

rezzie-rich

Dec 19, 2023

Thank you, I appreciate that.

thesunday

EmbeddedLLM org Jan 3, 2024

We merged another model with the CC models excluded.

thesunday changed discussion status to closed Jan 3, 2024

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