Instructions to use jaydeepb/pythia-1.4b-owm-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaydeepb/pythia-1.4b-owm-run2 with Transformers:

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

pipe = pipeline("text-generation", model="jaydeepb/pythia-1.4b-owm-run2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaydeepb/pythia-1.4b-owm-run2")
model = AutoModelForCausalLM.from_pretrained("jaydeepb/pythia-1.4b-owm-run2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaydeepb/pythia-1.4b-owm-run2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaydeepb/pythia-1.4b-owm-run2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaydeepb/pythia-1.4b-owm-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jaydeepb/pythia-1.4b-owm-run2

SGLang

How to use jaydeepb/pythia-1.4b-owm-run2 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 "jaydeepb/pythia-1.4b-owm-run2" \
    --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": "jaydeepb/pythia-1.4b-owm-run2",
		"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 "jaydeepb/pythia-1.4b-owm-run2" \
        --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": "jaydeepb/pythia-1.4b-owm-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jaydeepb/pythia-1.4b-owm-run2 with Docker Model Runner:
```
docker model run hf.co/jaydeepb/pythia-1.4b-owm-run2
```

pythia-1.4b-owm-run2

Pythia-1.4B fine-tuned for one epoch on a 1M-sequence (128-token) sample of OpenWebMath.

This is a run2 model, fine-tuned on a 1M sample drawn disjoint from run1. It provides the fresh held-out data used to evaluate the memorization-prediction classifier (predict-before-training validation). The matching run1 model supplies the labels used to train the classifier.

Base model, EleutherAI/pythia-1.4b
Dataset, OpenWebMath (open-web-math/open-web-math)
Training, 1 epoch, seq len 128, learning rate 5e-5, cosine schedule, seed 42

Downloads last month: 154

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for jaydeepb/pythia-1.4b-owm-run2

Base model

EleutherAI/pythia-1.4b

Finetuned

(93)

this model

Dataset used to train jaydeepb/pythia-1.4b-owm-run2

Collection including jaydeepb/pythia-1.4b-owm-run2

Predicting Memorization: Fine-tuned Models

Collection

33 items • Updated 1 day ago