79 lines
2.9 KiB
Markdown
79 lines
2.9 KiB
Markdown
---
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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base_model:
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- atenareply/smollm2-135m-noval
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- HuggingFaceTB/SmolLM2-135M
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datasets:
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- atenareply/noval-corp-sft-small
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- HuggingFaceTB/smol-smoltalk
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tags:
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- smollm2
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- sft
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- instruction-tuning
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- chatml
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license: apache-2.0
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---
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# SmolLM2-135M Noval — Instruct (SFT)
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Instruction-tuned domain assistant: SFT of the 135M CPT model on grounded domain instruction pairs blended with general instructions.
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## Overview
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- **Stage**: SFT (TRL SFTTrainer, ChatML, assistant-only loss)
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- **Lineage**: SmolLM2-135M → CPT (noval) → **SFT** (this model)
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- **Method**: SFT from the CPT'd checkpoint. ChatML with a `{% generation %}` block + assistant-only loss; grounded instruction pairs (teacher-distilled, generator≠judge, verbatim-number grounding filter [[genie]](#citations)) blended 60/40 with smol-smoltalk [[collapse]](#citations).
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- **Domain**: fictional — Orbital Mining Corporation (OMC) technical docs + Mars Express telemetry.
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## Training
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| Dataset | noval-corp-sft-small — train 1,388 / val 43 (chat `messages`) |
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| LR / epochs | 2e-5 cosine, warmup 0.03, 3 epochs, max_len 2048, eff_batch 32 |
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## Evaluation
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| Metric | Value | Note |
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| train_loss | **2.257** | healthy curve, no overfit |
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| eval_loss | **1.946** | per-epoch 2.048 → 1.959 → 1.946 |
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| token accuracy | **0.57** | 0.47 → 0.57 |
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_Eval is a held-out, deterministic verifiable harness (synthetic tasks); baseline = the pre-SFT ParamΔ model. See `noval-corp/scripts/eval_agentic.py`._
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## Intended use & limitations
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Domain Q&A / summarization / code-explanation over OMC / Mars-Express, in chat (ChatML) format.
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**Limitations:**
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- 135M instruct is intrinsically limited.
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- Instruction-following eval was handed off externally (no in-repo task metrics).
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- Fictional domain; sparse one-off entities hallucination-prone.
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## Innovations tested
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- **Local-subscription teacher distillation** — grounded pairs generated by Claude Code (generator ≠ judge), with a rule-based verbatim-number grounding gate [[genie]](#citations).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("atenareply/smollm2-135m-noval-instruct")
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model = AutoModelForCausalLM.from_pretrained("atenareply/smollm2-135m-noval-instruct")
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msgs = [{"role": "user", "content": "What does the Orbital Mining Corporation do?"}]
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inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
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print(tok.decode(model.generate(**inputs, max_new_tokens=256)[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## Citations
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- GENIE (grounded synthesis) — Mirza et al., ICLR 2024. arXiv:2401.14367
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- Model collapse (≥50% real data) — Shumailov et al., 2024. arXiv:2404.01413
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---
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_Card generated by `noval-corp/scripts/gen_model_cards.py` (standardized across the noval-corp model family)._ |