evenfarther/DeepSeek-R1-Distill-Qwen-14b-chemical-synthesis-PN-adapter

DeepSeek-R1-Distill-Qwen-14B Chemical Synthesis Classifier — LoRA Adapter (Epoch 9)

Model Overview

This repository contains the LoRA adapter for a DeepSeek‑R1‑Distill‑Qwen‑14B model fine‑tuned to classify chemical synthesizability (P = synthesizable, N = unsynthesizable). Training uses QLoRA on an imbalanced P/N dataset; evaluation scores each example with logsumexp(P) − logsumexp(N) at the final token under the SFT‑aligned prompt.

• —Task: Binary classification (P vs N)
• —Objective: QLoRA with class‑imbalance handling
• —Max sequence length (eval): 180 tokens
• —Dataset (train): train_llm_pn.jsonl
• —Dataset (val): valid_llm_pn.jsonl
• —Checkpoint: Epoch 9 (best Balanced Accuracy among evaluated epochs)

The checkpoint includes a chat_template.jinja to ensure prompt formatting matches SFT conditions.

Model Overview

This repository contains the LoRA adapter for the chemical synthesis classifier fine-tuned with focal loss. Training prompts follow the template:

You are a materials science assistant. Given a chemical composition, answer only with 'P' (synthesizable/positive) or 'N' (non-synthesizable/negative)." Correspondingly, each user query was formatted as: "Is the material {composition} likely synthesizable? Answer with P (positive) or N (negative).


Implementation notes for faithful scoring/inference:

- Build inputs via the chat template; drop the final assistant label from dataset inputs and tokenize with an empty assistant turn.
- Use `add_generation_prompt=False` and read logits right after the assistant start (trim the trailing EOS if present).
- Force `attn_implementation="eager"` for stability.

## Validation Metrics (Epoch 4 — Best)

| Metric              | Value      |
| ------------------- | ---------- |
| TPR (P Recall)      | **0.9750** |
| TNR (N Specificity) | 0.9556     |

## How to Load (Transformers + PEFT)

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

base = "unsloth/DeepSeek-R1-Distill-Qwen-14B-bnb-4bit" # Base model repo or equivalent local path

bnb = BitsAndBytesConfig( loadin4bit=True, bnb4bitquanttype="nf4", bnb4bitusedouble_quant=True, )

tok = AutoTokenizer.frompretrained(base, usefast=True) tok.paddingside = "right" if tok.padtoken is None: tok.padtoken = tok.eostoken

model = AutoModelForCausalLM.frompretrained( base, quantizationconfig=bnb, devicemap="auto", attnimplementation="eager", ) model = PeftModel.from_pretrained(model, adapter) model.eval()

Training Setup (Summary)

• —Base model: Unsloth "DeepSeek‑R1‑Distill‑Qwen‑14B‑bnb‑4bit" (4‑bit NF4)
• —Fine‑tuning: QLoRA via PEFT
• —Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• —LoRA config: r=32, alpha=32, dropout=0.0
• —Objective: P/U‑only Focal Loss applied to the last P or N token
• —gamma=2.0, alpha_P=7.5, alpha_N=1.0
• —Tokenizer: right‑padding; pad_token = eos_token if undefined

Dataset Sources

The training/validation corpus combines multiple public sources and internal curation:

• —P/U labelled data from J. Am. Chem. Soc. 2024, 146, 29, 19654-19659 (doi:10.1021/jacs.4c05840)
• —High-entropy materials data from Data in Brief 2018, 21, 2664-2678 (doi:10.1016/j.dib.2018.11.111)
• —Additional candidates via literature queries and manual screening of high-entropy materials

VRAM & System Requirements

• —GPU VRAM: ≥16 GB recommended (4‑bit base + adapter)
• —RAM: ≥16 GB recommended for tokenization and batching
• —Libraries: transformers, peft, bitsandbytes (evaluation uses transformers loader)
• —Set attn_implementation="eager" to avoid SDPA instability

Limitations & Notes

• —The adapter targets chemical synthesizability judgments; generalization outside this domain is not guaranteed.
• —For consistent results, use the included chat_template.jinja and avoid inserting <think> tokens.
• —Do not mutate the base model config.json (e.g., model_type), which can reinitialize weights and corrupt metrics.