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<title>LLaMA 3.2 3B + QLoRA — Python code generation</title>
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<h1>Python code generation — LLaMA 3.2 3B + QLoRA</h1>
<p class="sub">A 9.2M-parameter LoRA adapter (0.285% of the model), trained in 1.88 hours on a free T4.</p>
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<span class="stat">HumanEval pass@1 <b>40.5% → 54.1%</b></span>
<span class="stat">Valid Python <b>57.5% → 100%</b></span>
<span class="stat">Peak VRAM <b>4.17 GB</b></span>
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<p class="sub">The base model writes correct code — it just writes it in JavaScript
about 40% of the time, wrapped in markdown fences. Pick a prompt:</p>
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<h3>Base LLaMA 3.2 3B <span class="tag bad" id="btag"></span></h3>
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<h3>Fine-tuned <span class="tag good">this adapter</span></h3>
<pre id="ft"></pre>
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<p class="note">
Outputs are pre-generated with greedy decoding, so they are deterministic and reproducible.
Free HuggingFace Spaces have no GPU; running a 3B model live would take roughly 60 seconds
per generation.<br><br>
Model and full evaluation:
<a href="https://huggingface.co/Raghul09/llama-code-gen-lora">Raghul09/llama-code-gen-lora</a>
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