khazarai
/

Social-RLHF

@@ -9,202 +9,84 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
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 ## How to Get Started with the Model
 Use the code below to get started with the model.
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 ### Framework versions
 - PEFT 0.17.1

 - transformers
 - trl
 - unsloth
+license: mit
+datasets:
+- ProlificAI/social-reasoning-rlhf
+language:
+- en
 ---
+# Model Card for Social RLHF
 ## Model Details
+This model is a fine-tuned version of Qwen2.5-0.5B-Instruct on the ProlificAI/social-reasoning-rlhf dataset using ORPO.
+The primary objective was to experiment with Reinforcement Learning from Human Feedback (RLHF) via ORPO, focusing on preference alignment.
+### Model Description
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** unsloth/Qwen2.5-0.5B-Instruct
+- **Fine-tuning Method**: ORPO (Offline Reinforcement Preference Optimization)
+- **Dataset**: ProlificAI/social-reasoning-rlhf
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from huggingface_hub import login
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+login(token="")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct",)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen2.5-0.5B-Instruct",
+    device_map={"": 0}, token=""
+)
+model = PeftModel.from_pretrained(base_model,"khazarai/Social-RLHF")
+prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+inputs = tokenizer(
+    [
+        prompt.format(
+            "You are an AI assistant that helps people find information",
+            "A stranger shares private information with you on public transportation. How might you respond sensitively?",
+            "",
+        )
+    ],
+    return_tensors="pt",
+).to("cuda")
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512)
+```
 ### Framework versions
 - PEFT 0.17.1