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---
license: apache-2.0
---
# Pythia 1.4B Based Reward Model
- base model: [andreaskoepf/pythia-1.4b-gpt4all-pretrain](https://huggingface.co/andreaskoepf/pythia-1.4b-gpt4all-pretrain)
- wandb: https://wandb.ai/open-assistant/reward-model/runs/kadgqj65
- checkpoint: 10k steps
Compute was generously provided by [Stability AI](https://stability.ai/)
### How to use
```python
# install open assistant model_training module (e.g. run `pip install -e .` in `model/` directory of open-assistant repository)
import model_training.models.reward_model # noqa: F401 (registers reward model for AutoModel loading)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
input_text = "<|prompter|>Hi how are you?<|endoftext|><|assistant|>Hi, I am Open-Assistant a large open-source language model trained by LAION AI. How can I help you today?<|endoftext|>"
inputs = tokenizer(input_text, return_tensors="pt")
score = rm(**inputs).logits[0].cpu().detach()
print(score)
```
### Datasets
```
datasets:
- oasst_export:
lang: "en,es,de,fr"
input_file_path: 2023-03-27_oasst_research_ready_synth.jsonl.gz
val_split: 0.1
- augment_oasst:
input_file_path: augmented_latin_cyrillic_oasst_2023-03-27_v2.jsonl
- anthropic_rlhf:
fraction: 0.1
max_val_set: 1000
- shp:
max_val_set: 1000
- hellaswag:
fraction: 0.5
max_val_set: 1000
- webgpt:
val_split: 0.05
max_val_set: 1000
- hf_summary_pairs:
fraction: 0.1
max_val_set: 250
```
(internal note: ignore (high) eval accuracy values of oasst_export, oasst-eval samples were part of training set)
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