Update README.md

README.md

@@ -1,7 +1,54 @@
 ---
 license: apache-2.0
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+# Pythia 1.4B Based Reward Model
+
+- base model: 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/)
+
+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)