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Model: khazarai/HeisenbergQ-0.5B-RL
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- sft
+- grpo
+- unsloth
+- physics
+license: apache-2.0
+datasets:
+- jilp00/YouToks-Instruct-Quantum-Physics-II
+language:
+- en
+base_model:
+- unsloth/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+---
+
+# Model Card for HeisenbergQ-0.5B
+
+## Model Details
+
+HeisenbergQ-0.5B is a fine-tuned version of Qwen2.5-0.5B-Instruct, optimized for quantum physics reasoning using GRPO reinforcement learning with custom reward functions.
+This model is trained to produce structured answers in XML format with <reasoning> and <answer> tags. It excels at step-by-step logical reasoning in physics-related problems.
+
+### Model Description
+
+
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Qwen/Qwen2.5-0.5B-Instruct
+- **Fine-Tuning Method:** GRPO with LoRA
+- **Domain**: Quantum Physics
+- **Dataset**: jilp00/YouToks-Instruct-Quantum-Physics-II
+
+
+## Uses
+
+### Direct Use
+
+- Primary: Solving and reasoning through quantum physics problems
+- Secondary: General scientific reasoning in math & physics
+- Not for: General-purpose conversation (model is specialized)
+
+ 
+## Bias, Risks, and Limitations
+
+- Trained only on ~1K samples (domain-specific)
+- May hallucinate outside physics domain
+- Small 0.5B parameter size = lightweight, but reasoning depth is limited compared to larger models
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("khazarai/HeisenbergQ-0.5B-RL")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/HeisenbergQ-0.5B-RL",
+    device_map={"": 0}
+)
+
+system = """
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+question = """
+What is the significance of setting mass equal to 1 in a quantum dynamical system, and how does it impact the formulation of the Hamiltonian and the operators?
+"""
+
+messages = [
+    {"role": "system", "content": system},
+    {"role": "user", "content": question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 1800,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+
+## Training Details
+
+### Training Procedure
+
+- Training Method: GRPO (Grouped Relative Policy Optimization)
+- Reward Models: Reasoning Quality Reward: Encourages logical markers & coherent chains of thought
+- Token Count Reward: Prevents under- or over-explaining
+- XML Reward: Enforces <reasoning> / <answer> format
+- Soft Format Reward: Ensures graceful handling of edge cases
+- Steps: ~390 steps, 3 epochs
+- Batch Size: 16 (with 2 generations per prompt)