Instructions to use v000000/MN-12B-Estrella-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/MN-12B-Estrella-v1 with Transformers:

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

pipe = pipeline("text-generation", model="v000000/MN-12B-Estrella-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/MN-12B-Estrella-v1")
model = AutoModelForCausalLM.from_pretrained("v000000/MN-12B-Estrella-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use v000000/MN-12B-Estrella-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/MN-12B-Estrella-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/MN-12B-Estrella-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/v000000/MN-12B-Estrella-v1

SGLang

How to use v000000/MN-12B-Estrella-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 "v000000/MN-12B-Estrella-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": "v000000/MN-12B-Estrella-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 "v000000/MN-12B-Estrella-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": "v000000/MN-12B-Estrella-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use v000000/MN-12B-Estrella-v1 with Docker Model Runner:
```
docker model run hf.co/v000000/MN-12B-Estrella-v1
```

would you consider publishing the intermediate models from step 1 and 2

by nlpguy - opened Aug 13, 2024

Discussion

nlpguy

Aug 13, 2024

I would love to experiment with different ways to perform the final merge without having to do all the intermediate parts.

nlpguy changed discussion title from would you consder uploading the intermediate models from step 1 and 2 to would you consider uploading the intermediate models from step 1 and 2 Aug 13, 2024

nlpguy changed discussion title from would you consider uploading the intermediate models from step 1 and 2 to would you consider publishing the intermediate models from step 1 and 2 Aug 13, 2024

v000000

Owner Aug 13, 2024

Sure, I'll unprivate MN-Part1 and MN-Part2 they are on my page.

nlpguy

Aug 14, 2024

Thanks :)

nlpguy changed discussion status to closed Aug 14, 2024

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