Instructions to use jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf with Transformers:

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

pipe = pipeline("text-generation", model="jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf")
model = AutoModelForCausalLM.from_pretrained("jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf

SGLang

How to use jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf 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 "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf" \
    --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": "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf",
		"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 "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf" \
        --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": "jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf with Docker Model Runner:
```
docker model run hf.co/jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf
```

hh-rlhf

This model is a fine-tuned version of vicgalle/gpt2-open-instruct-v1 on an subset (15k) of the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 2.1534

This model responds to the 'Human:' or 'Assistant:' prompt pretty well in conversation situations.

The shorter responses are better suited. Keep generation length to a reasonable subset. Left to its own devices it will have some pretty esoteric responses.

These include fairly uncensored remarks and at times violent outbursts. Especially if asking questions.

Needs vetting for other textual uses.

Human: Insane clown posse says...

Human: Should we look for a woman?

Assistant: It’s okay if you’re having a tough time finding what you are looking for. It’s a common question people might come up with for an argument or misunderstanding. What are you looking for, and what kind of woman would you have?

Human: Are you trying to find someone to argue

Model description

GPT2 open instruct was trained on the open-instruct dataset fully. The reimagines one LM head as a partial rhlf adapter, with subtle reinforcements.

Intended uses & limitations

Intended to study the intersection of instruct models and prompting that focuses on subtle exchanges of prompting. This probably needs to be refined substantially at this point.

Training and evaluation data

Train dataset size: 15000
Test dataset size: 500
Dataset({
    features: ['chosen', 'rejected'],
    num_rows: 15000
})
Dataset({
    features: ['chosen', 'rejected'],
    num_rows: 500
})

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 2
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
2.3108	1.0	7500	2.1799
2.265	2.0	15000	2.1632
2.2507	3.0	22500	2.1567
2.2519	4.0	30000	2.1534

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

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vicgalle/gpt2-open-instruct-v1

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