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Model: towardtype1/qwen2.5-0.5b-gsm8k Source: Original Platform
2026-08-15 21:53:18 +08:00
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--- a/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).
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.0"
+}
--- a/merges.txt
+++ b/merges.txt
--- a/model.safetensors
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+oid sha256:a09bc2095f4fbd69f9b578f0dc592a537ec466856ce5bf87e0dedeaee48ecfd8
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--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+    },
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+    },
+    "151658": {
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+    "151660": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
--- a/vocab.json
+++ b/vocab.json