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---
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.
### 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 |
|