Instructions to use ethzanalytics/pythia-31m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/pythia-31m with Transformers:

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

pipe = pipeline("text-generation", model="ethzanalytics/pythia-31m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/pythia-31m")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/pythia-31m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethzanalytics/pythia-31m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/pythia-31m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/pythia-31m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/pythia-31m

SGLang

How to use ethzanalytics/pythia-31m 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 "ethzanalytics/pythia-31m" \
    --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": "ethzanalytics/pythia-31m",
		"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 "ethzanalytics/pythia-31m" \
        --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": "ethzanalytics/pythia-31m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/pythia-31m with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/pythia-31m
```

pythia-31m (fp32)

This is EleutherAI/pythia-31m but saved explicitly in fp32 - see safetensors params. It is smaller than the other 'official' checkpoints included in the Pythia study.

config/info

{
  "_name_or_path": "EleutherAI/pythia-31m",
  "architectures": [
    "GPTNeoXForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 0,
  "classifier_dropout": 0.1,
  "eos_token_id": 0,
  "hidden_act": "gelu",
  "hidden_dropout": 0.0,
  "hidden_size": 256,
  "initializer_range": 0.02,
  "intermediate_size": 1024,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 2048,
  "model_type": "gpt_neox",
  "num_attention_heads": 8,
  "num_hidden_layers": 6,
  "rope_scaling": null,
  "rotary_emb_base": 10000,
  "rotary_pct": 0.25,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.33.1",
  "use_cache": true,
  "use_parallel_residual": true,
  "vocab_size": 50304
}

Downloads last month: 20

Safetensors

Model size

30.5M params

Tensor type

F32

Model tree for ethzanalytics/pythia-31m

Base model

EleutherAI/pythia-31m

Finetuned

(11)

this model

ethzanalytics
/

pythia-31m

pythia-31m (fp32)

config/info

Model tree for ethzanalytics/pythia-31m

Dataset used to train ethzanalytics/pythia-31m