oasst-rm-2-pythia-1.4b-10000

How to use:

# 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)

wandb: https://wandb.ai/open-assistant/reward-model/runs/hdp2gnko checkpoint-10000

configuration:

oasst-rm-1-pythia-1.4b:
  is_reward_model: true
  pooling: last
  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.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:
        fraction: 0.1
        max_val_set: 250
  use_custom_sampler: true
  sort_by_length: false
  model_name: andreaskoepf/pythia-1.4b-gpt4all-pretrain
  learning_rate: 8e-6
  residual_dropout: 0.01
  weight_decay: 0.0
  dtype: float32
  max_length: 2048
  use_flash_attention: true
  warmup_steps: 50
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 1
  per_device_eval_batch_size: 5
  num_train_epochs: 2
  eval_steps: 500
  save_steps: 1000