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README.md

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+---
+license: other
+license_name: llama3.2
+license_link: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE
+base_model: unsloth/Llama-3.2-1B
+tags:
+  - math
+  - fine-tuned
+  - lora
+  - unsloth
+  - llama
+datasets:
+  - MathLLMs/MathCodeInstruct
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+model-index:
+  - name: Llama-3.2-1B-MathCodeInstruct-{{SIZE}}
+    results:
+      - task:
+          type: text-generation
+          name: GSM8K
+        dataset:
+          type: gsm8k
+          name: GSM8K
+        metrics:
+          - type: exact_match
+            value: {{GSM8K_ACC}}
+            name: exact match (flexible-extract, 5-shot)
+      - task:
+          type: text-generation
+          name: ARC-Challenge
+        dataset:
+          type: ai2_arc
+          name: ARC-Challenge
+        metrics:
+          - type: acc_norm
+            value: {{ARC_ACC}}
+            name: acc_norm (25-shot)
+      - task:
+          type: text-generation
+          name: HellaSwag
+        dataset:
+          type: hellaswag
+          name: HellaSwag
+        metrics:
+          - type: acc_norm
+            value: {{HELLASWAG_ACC}}
+            name: acc_norm (10-shot)
+      - task:
+          type: text-generation
+          name: WinoGrande
+        dataset:
+          type: winogrande
+          name: WinoGrande
+        metrics:
+          - type: acc
+            value: {{WINOGRANDE_ACC}}
+            name: acc (5-shot)
+      - task:
+          type: text-generation
+          name: MMLU
+        dataset:
+          type: mmlu
+          name: MMLU
+        metrics:
+          - type: acc
+            value: {{MMLU_ACC}}
+            name: acc (5-shot)
+---
+
+# Llama-3.2-1B-MathCodeInstruct-10k
+
+A [Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B) fine-tune on **10k examples** from
+[MathLLMs/MathCodeInstruct](https://huggingface.co/datasets/MathLLMs/MathCodeInstruct), trained to solve math
+word problems with step-by-step natural-language reasoning interleaved with executable Python.
+
+This is one of three sibling models trained on {5k, 10k, 20k}-example subsets of the same dataset, to study how
+fine-tuning data volume trades off against both math performance and general capability. See the
+[training write-up](https://github.com/OliverSundaram/finetuning-Llama3.2-1B) for the full comparison across all three.
+
+## Training details
+
+| |                                                                                         |
+|---|-----------------------------------------------------------------------------------------|
+| Base model | `unsloth/Llama-3.2-1B`                                                                  |
+| Method | LoRA (r=16, α=16, dropout=0) on all attention + MLP projections, merged to full weights |
+| Dataset | MathLLMs/MathCodeInstruct, 10k training examples                                        |
+| Epochs | 1                                                                                       |
+| Effective batch size | 16 (batch 1 × grad. accum. 16)                                                          |
+| Learning rate | 2e-4, cosine schedule, warmup ratio 0.03                                                |
+| Hardware | 1× RTX 4060 (8GB)                                                                       |
+| Framework | Unsloth + TRL `SFTTrainer`                                                              |
+
+## Benchmark results
+
+All benchmarks run with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), each at
+its standard published shot count, compared against the un-tuned base model.
+
+| Benchmark | Llama-3.2-1B (base) | This model | Change |
+|---|---|---|---|
+| GSM8K | 5.8% | 8.7% | 🟢 +2.9% |
+| ARC-Challenge | 36.9% | 36.1% | 🔴 -0.8% |
+| HellaSwag | 64.2% | 63.8% | 🔴 -0.4% |
+| WinoGrande | 60.8% | 62.0% | 🟢 +1.3% |
+
+**Speed** (single-request generation, greedy, RTX 4060): **38.79 tokens/sec**
+(base model: 12.74 tokens/sec)
+
+### MMLU by category
+
+![MMLU comparison](mmlu_10k.png)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-10k}"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
+
+messages = [
+    {"role": "system", "content": "Below is a math problem. Please solve it step by step."},
+    {"role": "user", "content": "If a train travels 60 miles in 45 minutes, what is its speed in miles per hour?"},
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+output = model.generate(inputs, max_new_tokens=512, do_sample=False)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Trained on a single epoch of a 10k-example subset — not intended to be a general-purpose assistant.
+- MMLU/ARC/HellaSwag/WinoGrande scores reflect a small 1B-parameter base model and should be read relative to
+  the base model's own scores, not against much larger models.
+- No safety alignment or RLHF was applied beyond what the base Llama-3.2-1B already has.