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+---
+language:
+- en
+license: apache-2.0
+base_model: paperbd/smollm_135M_arxiv_cpt
+tags:
+- sft
+- instruction-tuning
+- lora
+- unsloth
+- scientific
+- arxiv
+- nlp
+- paper-researcher
+datasets:
+- paperbd/paper_instructions_300K-v1
+---
+
+# SmolLM-135M-SFT-exp01
+
+Supervised fine-tuning of [SmolLM-135M-CPT-LoRA-r32](https://huggingface.co/JaydeepR/SmolLM-135M-CPT-LoRA-r32) on 300K synthetic ML paper instruction pairs.
+
+This is **exp01** in a series of SFT experiments on top of the CPT-adapted SmolLM-135M.
+
+## Model Description
+
+- **Base model:** `paperbd/smollm_135M_arxiv_cpt` (CPT-adapted SmolLM-135M, merged)
+- **Method:** Supervised Fine-Tuning (SFT) with LoRA + `train_on_responses_only`
+- **Domain:** ML/arXiv paper research tasks
+- **Task:** Instruction following — bullets, QA, triplets, retrieval, comparison, etc.
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| LoRA rank | 32 |
+| LoRA alpha | 32 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Trainable params | ~9.7M / 144M (6.77%) |
+| Quantization | 4-bit (QLoRA via Unsloth) |
+| Batch size | 32 |
+| Gradient accumulation | 4 (effective batch: 128) |
+| Learning rate | 2e-4 (linear decay) |
+| Warmup ratio | 0.03 |
+| Epochs | 3 |
+| Total steps | 11,355 |
+| Sequence length | 2048 (packed) |
+| Chat template | ChatML |
+| Hardware | NVIDIA RTX 4090 |
+| Training time | ~10 hours |
+
+## Training Data
+
+- **Dataset:** `paperbd/paper_instructions_300K-v1` — 300K synthetic instruction-response pairs generated from arXiv ML papers
+- **Variations:** 2 (conversation extension) → ~600K effective training examples
+- **Train/val split:** 98% / 2%
+- **Response-only training:** Loss computed only on assistant turns, not user prompts
+
+## Evaluation Results
+
+Evaluated on 1000 samples from the `paper_instructions_300K-v1` test split, judged by `grok-3-mini`:
+
+| Metric | Score (1-5) |
+|---|---|
+| Faithfulness | 2.70 |
+| Answer Correctness | 1.98 |
+| Relevance | 3.04 |
+| Completeness | 1.85 |
+| **Overall** | **2.39** |
+
+## How to Use
+
+### As PaperResearcher API
+
+```python
+from paper_researcher import PaperResearcher
+
+researcher = PaperResearcher("JaydeepR/SmolLM-135M-SFT-exp01")
+
+passage = "Attention mechanisms compute weighted sums of values..."
+
+bullets = researcher.extract_bullets(passage)
+qa_pairs = researcher.generate_qa_pairs(passage)
+triplets = researcher.extract_triplets(passage)
+answer = researcher.answer("What does attention compute?", passage)
+```
+
+### Raw inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+adapter_id = "JaydeepR/SmolLM-135M-SFT-exp01"
+base_model_id = "paperbd/smollm_135M_arxiv_cpt"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+model = AutoModelForCausalLM.from_pretrained(base_model_id)
+model = PeftModel.from_pretrained(model, adapter_id)
+
+messages = [
+    {"role": "system", "content": "You are an expert in AI and ML research."},
+    {"role": "user", "content": "Extract the key points from this passage as bullet points.\n\nAttention mechanisms..."},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Supported Tasks
+
+| Task | Method |
+|---|---|
+| Extract bullet points | `researcher.extract_bullets(passage)` |
+| Generate Q&A pairs | `researcher.generate_qa_pairs(passage)` |
+| Generate a question | `researcher.generate_question(passage)` |
+| Extract a fact | `researcher.extract_fact(passage)` |
+| Answer a question | `researcher.answer(question, passage)` |
+| Rephrase passage | `researcher.rephrase(passage)` |
+| Continue passage | `researcher.continue_from(passage_start)` |
+| Extract knowledge graph | `researcher.extract_triplets(passage)` |
+| Compare two passages | `researcher.compare(passage_a, passage_b)` |
+| Retrieval | `researcher.find_relevant(question, passages)` |
+
+## Limitations
+
+- 135M parameter model — limited factual recall and reasoning
+- Trained on synthetic data — may not generalise to all instruction styles
+- Relevance is the strongest dimension (3.04/5); correctness and completeness are weak (< 2/5)
+- Best used for structured extraction tasks, not open-ended QA
+
+## Citation
+
+```
+@misc{smollm135m-sft-exp01,
+  author = {Jaydeep Raijada},
+  title  = {SmolLM-135M SFT exp01 — Instruction Tuning on ML Paper Research Tasks},
+  year   = {2026},
+  url    = {https://huggingface.co/JaydeepR/SmolLM-135M-SFT-exp01}
+}
+```