frupniew/macaulay2-rag-coder-3b-adapter

---

base_model:

• Qwen/Qwen2.5-Coder-3B-Instruct library_name: peft pipeline_tag: text-generation tags:
• lora
• sft
• transformers
• unsloth
• qlora
• macaulay2
• peft
• code-generation license: mit datasets:
• frupniew/macaulay2-qa-instruct language:
• en

Macaulay2 RAG-Coder 3B (LoRA Adapter)

This repository contains the QLoRA adapter weights trained to turn Qwen2.5-Coder-3B-Instruct into a domain expert for Macaulay2 (commutative algebra).

🛠️ Training Details & Post-Mortem

• Framework: Unsloth (QLoRA 4-bit)
• Hyperparameters: r=16, alpha=32, lr=2e-4, epochs=5, packing=True (max_seq_len=4096).
• Target Modules: All linear layers.
• Engineering Post-Mortem (Task Mismatch): Initial training resulted in a model that perfectly understood M2 syntax but wrapped outputs in JSON (due to the synthetic data generator's system prompt). A continual learning retrain (Path A) was executed on pure ChatML formatted data to force raw Markdown code blocks, successfully resolving the issue.
• PEFT Quirk Handled: Addressed the tie_word_embeddings issue (PEFT #2777) specific to Qwen2.5 architecture during the merge process to prevent generation degradation.

🐍 Usage with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-Coder-3B-Instruct"
adapter_model_id = "frupniew/macaulay2-rag-coder-3b-adapter"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_model_id)

prompt = "How do I compute the primary decomposition of an ideal?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))