Instructions to use lomahony/pythia-70m-helpful-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lomahony/pythia-70m-helpful-sft with Transformers:

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

pipe = pipeline("text-generation", model="lomahony/pythia-70m-helpful-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lomahony/pythia-70m-helpful-sft")
model = AutoModelForCausalLM.from_pretrained("lomahony/pythia-70m-helpful-sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lomahony/pythia-70m-helpful-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lomahony/pythia-70m-helpful-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lomahony/pythia-70m-helpful-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lomahony/pythia-70m-helpful-sft

SGLang

How to use lomahony/pythia-70m-helpful-sft 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 "lomahony/pythia-70m-helpful-sft" \
    --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": "lomahony/pythia-70m-helpful-sft",
		"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 "lomahony/pythia-70m-helpful-sft" \
        --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": "lomahony/pythia-70m-helpful-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lomahony/pythia-70m-helpful-sft with Docker Model Runner:
```
docker model run hf.co/lomahony/pythia-70m-helpful-sft
```

Pythia-70m supervised finetuned using TRLx library with the helpful subset of Anthropic-hh-rlhf dataset for 1 epoch.

Checkpoints are also uploaded.

Fully reproducible finetuning code is available on GitHub

wandb log

See Pythia-70m for model details (paper).

See further details of these models in the paper Attributing Mode Collapse in the Fine-Tuning of Large Language Models.

You can cite these models if they are helpful as follows:

@inproceedings{o2024attributing,
  title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
  author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
  booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
  year={2024}
}

hf (pretrained=lomahony/pythia-70m-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16

Tasks	Version	Filter	Metric	Value		Stderr
arc_challenge	1	none	acc	0.1715	±	0.0110
		none	acc_norm	0.2082	±	0.0119
arc_easy	1	none	acc	0.3384	±	0.0097
		none	acc_norm	0.3262	±	0.0096
boolq	2	none	acc	0.4239	±	0.0086
hellaswag	1	none	acc	0.2629	±	0.0044
		none	acc_norm	0.2691	±	0.0044
lambada_openai	1	none	perplexity	5937.7964	±	424.7555
		none	acc	0.0328	±	0.0025
openbookqa	1	none	acc	0.1580	±	0.0163
		none	acc_norm	0.2520	±	0.0194
piqa	1	none	acc	0.5593	±	0.0116
		none	acc_norm	0.5392	±	0.0116
sciq	1	none	acc	0.3710	±	0.0153
		none	acc_norm	0.4990	±	0.0158
wikitext	2	none	word_perplexity	550.5954	±	N/A
		none	byte_perplexity	3.2550	±	N/A
		none	bits_per_byte	1.7027	±	N/A
winogrande	1	none	acc	0.4878	±	0.0140

hf (pretrained=lomahony/pythia-70m-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
arc_challenge	1	none	5	acc	0.1869	±	0.0114
		none	5	acc_norm	0.2210	±	0.0121
arc_easy	1	none	5	acc	0.3207	±	0.0096
		none	5	acc_norm	0.3245	±	0.0096
boolq	2	none	5	acc	0.4159	±	0.0086
hellaswag	1	none	5	acc	0.2633	±	0.0044
		none	5	acc_norm	0.2596	±	0.0044
lambada_openai	1	none	5	perplexity	19968.0749	±	1423.3001
		none	5	acc	0.0202	±	0.0020
openbookqa	1	none	5	acc	0.1440	±	0.0157
		none	5	acc_norm	0.2420	±	0.0192
piqa	1	none	5	acc	0.5359	±	0.0116
		none	5	acc_norm	0.5229	±	0.0117
sciq	1	none	5	acc	0.3240	±	0.0148
		none	5	acc_norm	0.4310	±	0.0157
wikitext	2	none	5	word_perplexity	550.5954	±	N/A
		none	5	byte_perplexity	3.2550	±	N/A
		none	5	bits_per_byte	1.7027	±	N/A
winogrande	1	none	5	acc	0.5154	±	0.0140

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Safetensors

Model size

70.4M params

Tensor type

BF16

Dataset used to train lomahony/pythia-70m-helpful-sft

Collection including lomahony/pythia-70m-helpful-sft

pythia-helpful-1epoch

Collection

Pythia-2.8b supervised finetuned and DPO finetuned with the helpful subset of Anthropic-hh-rlhf dataset for 1 epoch. • 12 items • Updated Mar 12, 2024

Paper for lomahony/pythia-70m-helpful-sft

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Paper • 2101.00027 • Published Dec 31, 2020 • 10