Instructions to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with Transformers:

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

pipe = pipeline("text-generation", model="jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6")

# Load model directly
from transformers import AutoTokenizer, LlamaForCausalLM_sharedHyper

tokenizer = AutoTokenizer.from_pretrained("jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6")
model = LlamaForCausalLM_sharedHyper.from_pretrained("jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6

SGLang

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 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 "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6" \
    --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": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"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 "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6" \
        --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": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with Docker Model Runner:
```
docker model run hf.co/jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6
```

Add pipeline tag, link to paper and GitHub repository

by nielsr HF Staff - opened Jun 20

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+21

-17

nielsr

Jun 20

This PR improves the model card by:

Adding the pipeline_tag: text-generation metadata tag so it is discoverable under the correct pipeline task.
Linking the model card to its respective paper on the Hugging Face Hub (https://huggingface.co/papers/2605.01973) and the open-source GitHub repository (https://github.com/AaronJi/MeGan).
Adding a model description and citation block based on the paper.

Add pipeline tag, link to paper and GitHub repository6cbe727a

jiluoaaron changed pull request status to merged Jun 21

jiluoaaron

Owner Jun 21

Merge

jiluoaaron deleted the refs/pr/1 ref Jun 23

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