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Model: towardtype1/qwen2.5-0.5b-gsm8k
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B
+datasets:
+- openai/gsm8k
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- math
+- gsm8k
+- grpo
+- lora
+- mlx
+model-index:
+- name: qwen2.5-0.5b-gsm8k
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: GSM8K
+      type: openai/gsm8k
+    metrics:
+    - type: accuracy
+      value: 0.416
+      name: GSM8K test, zero-shot, strict `#### answer` match
+---
+
+# qwen2.5-0.5b-gsm8k
+
+[Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) (base) post-trained for grade-school math on a MacBook Pro, with every training loop written by hand in [MLX](https://github.com/ml-explore/mlx) as a learning exercise.
+
+Two stages, both via rank-16 LoRA on the attention q/v projections (1,081,344 trainable parameters, 0.2% of the model), adapters merged into the weights afterwards:
+
+1. **SFT** on GSM8K's worked solutions (900 steps, lr 1e-4).
+2. **GRPO** with verifiable rewards - 1.0 if the extracted `#### answer` matches gold, else 0.0 - with a k3 KL penalty against the frozen SFT reference (100 steps, groups of 8 at temperature 0.8, lr 1e-5, kl 0.1, grad clip 1.0).
+
+## Results (GSM8K test, zero-shot, greedy, strict `#### <number>` match)
+
+| Model | Accuracy |
+|---|---|
+| Qwen2.5-0.5B base | 0.000 (0.305 with 4-shot prompting) |
+| + LoRA SFT | 0.345 |
+| **+ LoRA GRPO (this model)** | **0.416** |
+| full-fine-tune SFT, for comparison | 0.325 |
+| full-fine-tune GRPO, for comparison | 0.303 |
+
+Full test set (1,319 questions) for all rows.
+The interesting finding: the same GRPO recipe that stalled on full fine-tuning gained 7 points under the low-rank constraint - the small trainable space acts as a structural leash on noisy small-batch policy gradients.
+
+## Usage
+
+Prompt format (the model is a narrow GSM8K-format specialist, not a chat model):
+
+```
+Question: <your word problem>
+Answer:
+```
+
+It emits step-by-step reasoning ending in `#### <number>`.
+
+With mlx-lm:
+
+```python
+from mlx_lm import load, generate
+model, tokenizer = load("towardtype1/qwen2.5-0.5b-gsm8k")
+print(generate(model, tokenizer, prompt="Question: Tom has 3 boxes of 12 pencils. He gives away 5. How many are left?\nAnswer:", max_tokens=200))
+```
+
+With transformers (weights are standard Qwen2 layout):
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("towardtype1/qwen2.5-0.5b-gsm8k")
+```
+
+## Limitations
+
+- Single-task specialist: trained and evaluated only on GSM8K-style word problems in the exact format above. It is not aligned, not a chat assistant, and inherits any base-model limitations and biases.
+- 0.5B parameters: arithmetic slips are common; verify answers.
+
+## Training write-up
+
+The full story - including two instructive GRPO failure modes (format collapse without a KL penalty, gradient-noise drift without clipping) and why the LoRA constraint fixed them - is in the blog post [Post-training from scratch](https://towardtype1.github.io/essays/post-training-from-scratch/) and the code repo [towardtype1/grade-school-math](https://github.com/towardtype1/grade-school-math).