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Model: LahiruWije/Qwen2.5-0.5B-Instruct-GPRO-GSM8K
Source: Original Platform

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README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+- trl
+- grpo
+- reasoning
+- gsm8k
+datasets:
+- openai/gsm8k
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: question-answering
+license: apache-2.0
+---
+
+# Model Card for Qwen2.5-0.5B-Instruct-GSM8K-Reasoning
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is a fine-tuned version of the **Qwen2.5-0.5B-Instruct** model, specifically adapted for **mathematical reasoning tasks** using the **GSM8K dataset**. It leverages **GPRO (Generalized Policy Optimization for Reasoning)** methods, as described in the *DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models* paper, to enhance its reasoning capabilities. The fine-tuning process was performed using **Unsloth** for efficiency and **TRL (Transformer Reinforcement Learning)** for reinforcement learning-based training.
+
+## Model Details
+
+## How to Get Started with the Model
+
+Use the code below to load and use the model with vLLM & Unsloth:
+
+```python
+from unsloth import FastLanguageModel
+from vllm import SamplingParams
+import torch
+
+# Load the Model & Tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "AdamLucek/Qwen2.5-3B-Instruct-GRPO-2K-GSM8K",
+    max_seq_length = 2048,
+    load_in_4bit = True,
+    fast_inference = True,
+    gpu_memory_utilization = 0.7,
+)
+
+# Prep the Message
+PROMPT = "How many r's are in the word strawberry?"
+
+SYSTEM_PROMPT = """
+A conversation between User and Assistant. The user asks a question,
+and the Assistant solves it. The assistant first thinks about the
+reasoning process in the mind and then provides the user with the answer.
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+text = tokenizer.apply_chat_template([
+    {"role" : "system", "content" : SYSTEM_PROMPT},
+    {"role" : "user", "content" : PROMPT},
+], tokenize = False, add_generation_prompt = True)
+
+# Generate a response
+sampling_params = SamplingParams(
+    temperature = 0.8,
+    top_p = 0.95,
+    max_tokens = 1024,
+)
+output = model.fast_generate(
+    text,
+    sampling_params = sampling_params,
+)[0].outputs[0].text
+```
+
+### Model Description
+
+- **Model type:** Transformer-based language model fine-tuned for mathematical reasoning.
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+
+## Uses
+
+### Direct Use
+
+This model is intended for **mathematical reasoning tasks**, particularly for solving grade-school-level math problems as found in the GSM8K dataset. It can be used directly for question-answering tasks involving arithmetic and reasoning.
+
+### Downstream Use [optional]
+
+The model can be fine-tuned further for specific applications, such as tutoring systems, automated problem-solving tools, or other educational technologies.
+
+### Out-of-Scope Use
+
+This model is not designed for:
+- High-level mathematical research or advanced problem-solving.
+- Non-mathematical reasoning tasks without additional fine-tuning.
+- Applications requiring high precision in domains outside its training data.
+
+## Bias, Risks, and Limitations
+
+- **Bias:** The model may inherit biases present in the GSM8K dataset or the base model.
+- **Risks:** Incorrect reasoning or answers in critical applications (e.g., education or finance) could lead to misinformation.
+- **Limitations:** The model's performance is constrained by the quality and scope of the GSM8K dataset and the base model's capabilities.
+
+### Recommendations
+
+Users should:
+- Validate the model's outputs for critical applications.
+- Fine-tune the model further for domain-specific tasks.
+- Be aware of potential biases and limitations in reasoning capabilities.
+
+## Citations
+
+Cite GRPO as:
+
+```bibtex
+@article{zhihong2024deepseekmath,
+    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+    year         = 2024,
+    eprint       = {arXiv:2402.03300},
+}
+
+```
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

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config.json

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+  "torch_dtype": "float16",
+  "transformers_version": "4.48.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.2.15",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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