---
license: apache-2.0
tags:
- generated_from_trainer
- HC3
- chatGPT
- assistant
datasets:
- pszemraj/HC3-textgen-qa
metrics:
- accuracy
inference: False
---

# pythia-6.9b-deduped for general QA

<a href="https://colab.research.google.com/gist/pszemraj/351f04fc2afb6346c763885f127284ef/pythia-6-9b-deduped-for-general-qa.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

This model is a fine-tuned version of [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) on the pszemraj/HC3-textgen-qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2372
- Accuracy: 0.6769

## Model description

Text generation model trained on the HC3 text data of human questions + chatGPT answers.

![example](https://i.imgur.com/iMqPDXU.png)


### Usage

Install necessary packages for inference (_unless you have a big boi GPU_)
```bash
pip install -U -q transformers bitsandbytes accelerate
```

Basic inference example:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3")

model = AutoModelForCausalLM.from_pretrained(
    "pszemraj/pythia-6.9b-HC3", load_in_8bit=True, device_map="auto"
) # shards are ~4GB each, there are eight total

prompt = "I was wondering how much wood a woodchuck could chuck? <answer>"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300) # default generation config (+ 300 tokens)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
result = result.split("<end_answer>")[0].strip()

import pprint as pp

pp.pprint(result)
```

The defautl `GenerationConfig` uses contrastive search with `top_k=4` and `penalty_alpha=0.6`. For more information on inference and parameters to use, see [the transformers docs](https://huggingface.co/docs/transformers/generation_strategies#decoding-strategies).

## Intended uses & limitations

- **Intended use:** research/exploration into comparing RLHF tuning vs. "guided"/specific tuning on "quality" datasets/responses of _"what the human would want as answer anyway"_
- This is **not** trained/fine-tuned with RLHF and therefore will not be as helpful/generalizable/safe as chatGPT (_outside of the fact that this model is ~30x smaller_)

## Training and evaluation data

```yaml
model-index:
- name: pythia-6.9b-hc3-qa-assistant
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: pszemraj/HC3-textgen-qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6768941789814655
```


## Training procedure

Two epochs on the `pszemraj/HC3-textgen-qa` dataset.

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2598        | 0.99  | 79   | 1.3291          | 0.6496   |
| 0.7446        | 1.99  | 158  | 1.2372          | 0.6769   |