
GPT-J (with value head weights) trained on HH with PPO following [@reciprocated's](https://github.com/reciprocated) `trlx` example [here](https://github.com/CarperAI/trlx/blob/2f90ba0ecd640ae18cd62adb5e934a4b779f534b/examples/hh/ppo_hh.py).

- Dataset: [Dahoas/full-hh-rlhf](https://huggingface.co/datasets/Dahoas/full-hh-rlhf)
- Logs: https://wandb.ai/jon-tow/trlx/reports/hh-gpt-j--VmlldzozODE1NjAw
- Notebook: https://colab.research.google.com/drive/1B-XKZv7h6u_pkyvckGocukEX5zLmACqc

Usage:

```python
from transformers import AutoTokenizer
from trlx.models.modeling_ppo import AutoModelForCausalLMWithHydraValueHead

model = AutoModelForCausalLMWithHydraValueHead.from_pretrained("jon-tow/hh-gpt-j")
# original_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")

tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

prompt_1 = """\
Human: Hello, can you help me?
Assistant: Sure, what can I do for you?
Human: I'm looking for a good recipe for a strawberry cake. What ingredients do I need?
Assistant:\
"""
prompt_2 = """\
Human: Hi! What kind of music do you like?
Assistant: I like all kinds of music.
Human: I'm trying to learn how to play the guitar. Do you have any tips?
Assistant:\
"""
prompts = [prompt_1, prompt_2]
inputs = tokenizer(
    [prompt_1, prompt_2],
    return_tensors="pt",
    padding=True,
)

samples = model.generate(
    **inputs,
    max_new_tokens=64,
    top_k=0,
    top_p=1.0,
    do_sample=True,
)

responses = []
prompt_tokens_lengths = [len(tokenizer.encode(prompt)) for prompt in [prompt_1, prompt_2]]
stop_sequences = ["Human:", "human:", "Assistant:", "assistant:"]
for i, sample in enumerate(samples):
    response = tokenizer.decode(sample[prompt_tokens_lengths[i]:], skip_special_tokens=True)
    # Trim off extra dialogue
    for stop in stop_sequences:
        stop_i = response.find(stop)
        if stop_i >= 0:
            response = response[:stop_i].rstrip()
    responses.append(response)

print()
for prompt, response in zip(prompts, responses):
    print("=" * 40)
    print(prompt + response)
    print("=" * 40)
    print()
```

Output:
```
========================================
Human: Hello, can you help me?
Assistant: Sure, what can I do for you?
Human: I'm looking for a good recipe for a strawberry cake. What ingredients do I need?
Assistant: Is strawberry flavour a primary flavour you want in the cake?
========================================

========================================
Human: Hi! What kind of music do you like?
Assistant: I like all kinds of music.
Human: I'm trying to learn how to play the guitar. Do you have any tips?
Assistant: One thing you can try is to form chords and strums. Form chords and strums will help you to practice and learn how to play instruments easily. You can also download free music online. Besure to check out different genres and instruments. You don't have to learn everyone all at once. Learning the basics is
========================================
```
{"language": ["en"], "license": "apache-2.0"}
transformers pytorch gptj text-generation en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #gptj #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


GPT-J (with value head weights) trained on HH with PPO following @reciprocated's 'trlx' example here.

- Dataset: Dahoas/full-hh-rlhf
- Logs: URL
- Notebook: URL

Usage:



Output:

TAGS
#transformers #pytorch #gptj #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #gptj #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
