khazarai
/

Psychology-RLHF

@@ -9,202 +9,108 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
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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:
+- samhog/psychology-RLHF
+language:
+- en
 ---
+# Model Card for Psychology-RLHF
 ### Model Description
+This model is a fine-tuned version of Qwen2.5-0.5B-Instruct on the samhog/psychology-RLHF dataset using ORPO.
+The primary objective was to experiment with Reinforcement Learning from Human Feedback (RLHF) via ORPO, focusing on preference alignment.
+The dataset comes from the psychology domain, but the main purpose of this fine-tuning was to study and demonstrate the effectiveness of ORPO for aligning small-scale instruction-tuned models.
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** unsloth/Qwen2.5-0.5B-Instruct
+- **Fine-tuning Method**: ORPO (Offline Reinforcement Preference Optimization)
+- **Dataset**: samhog/psychology-RLHF
+- **Domain**: Psychology, mental health reasoning, and conversational alignment
 ## Uses
 ### Direct Use
+- Educational and research purposes in psychology-related question-answering.
+- Conversational agents for safe psychology discussions.
+- Research on RLHF and ORPO fine-tuning in domain-specific contexts.
 ## Bias, Risks, and Limitations
+- This model is not a substitute for professional mental health advice.
+- Trained on synthetic/human preference data → may still generate biased or hallucinated content.
+- Small-scale model (0.5B parameters) → limited reasoning ability compared to larger LLMs.
 ## How 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/Psychology-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",
+            "I'm having trouble with my teenage child. They're acting out and I don't know what to do.",
+            "",
+        )
+    ],
+    return_tensors="pt",
+).to("cuda")
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512)
+```
+## Training Details
+Training Metrics:
+- Training Loss: ↓ from 1.86 → 0.2978
+- NLL Loss: ↓ from 1.77 → 0.34
+- Reward (Chosen): -0.19 → -0.037
+- Reward (Rejected): -0.20 → -0.150
+- Reward Gap: ≈ +0.11
+Interpretation:
+- Losses decreased steadily, indicating stable convergence.
+- Chosen rewards improved toward 0, while rejected remained lower, showing preference alignment.
+- Final model demonstrates improved distinction between good vs. bad responses.
 ### Framework versions
 - PEFT 0.17.1