Instructions to use Multi-Domain-Expert-Learning/expert-pubmed_central with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/expert-pubmed_central with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/expert-pubmed_central")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/expert-pubmed_central")
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/expert-pubmed_central")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multi-Domain-Expert-Learning/expert-pubmed_central with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/expert-pubmed_central

SGLang

How to use Multi-Domain-Expert-Learning/expert-pubmed_central 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 "Multi-Domain-Expert-Learning/expert-pubmed_central" \
    --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": "Multi-Domain-Expert-Learning/expert-pubmed_central",
		"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 "Multi-Domain-Expert-Learning/expert-pubmed_central" \
        --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": "Multi-Domain-Expert-Learning/expert-pubmed_central",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/expert-pubmed_central with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/expert-pubmed_central
```

layer_9,10,11,12,13

This model is a fine-tuned version of EleutherAI/pythia-1b-deduped on the Multi-Domain-Expert-Layers/pubmed_central dataset. It achieves the following results on the evaluation set:

Loss: 2.0227
Accuracy: 0.5768

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0567	0.0	200	2.0533	0.5717
2.041	0.01	400	2.0438	0.5733
2.0496	0.01	600	2.0361	0.5749
2.0194	0.02	800	2.0276	0.5761
2.0338	0.02	1000	2.0227	0.5768

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

Wandb Report

https://wandb.ai/ontocord/pythia-1b-deduped-layer-test-pubmed_central/runs/yy3pwx0o

Downloads last month: 8

Evaluation results

Accuracy on Multi-Domain-Expert-Layers/pubmed_central
self-reported

0.577