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smollm2-360m-unsloth-essay-scoring-finetune – AI Model by samodelkini | AlphaNeural AI

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samodelkini/
smollm2-360m-unsloth-essay-scoring-finetune

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persuasive-writing

text-generation

unsloth/SmolLM2-360M-Instruct

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smollm2-360m-unsloth-essay-scoring-finetune

LoRA adapter for unsloth/SmolLM2-360M-Instruct trained for the local multi-agent essay grading workflow in this repository.

This is not a standalone full model. It is an adapter intended to be loaded on top of the base model.

Training setup

Base model: unsloth/SmolLM2-360M-Instruct
Training method: Unsloth + PEFT LoRA
Task type: causal LM
LoRA rank: 8
LoRA alpha: 16
LoRA dropout: 0.0
Target modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Max length: 4096
Epochs: 2
Learning rate: 2e-4
Gradient accumulation: 8
Quantized loading during training: 4-bit

Training data

The adapter was trained from teacher outputs stored in:

results/OLMo_PERSUADE_1200_results.json

with essays drawn from:

corpus/persuade/persuade_sample_1200.json

The run config bundled with this adapter is in distillation_unsloth_run_config.json.

Evaluation

Evaluation was run through the repository's existing multi-agent grading pipeline.

GRE subset

File: results/original_magic/GRE_essays_smollm2_360m_unsloth_secondversion.json
n_final: 48
Final QWK: 0.6092
Final RMSE: 1.1902
Exact agreement: 0.3333
Adjacent agreement: 0.7500

Persuade subset

File: results/original_magic/persuade_48_smollm2_360m_unsloth_secondversion.json
n_final: 48
Final QWK: 0.6720
Final RMSE: 1.1273
Exact agreement: 0.3542
Adjacent agreement: 0.7917

Summary metrics are stored in:

results/original_magic/smollm2_360m_unsloth_secondversion_numeric_eval_summary.csv

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "unsloth/SmolLM2-360M-Instruct"
adapter_path = "samodelkini/smollm2-360m-unsloth-essay-scoring-finetune"

tokenizer = AutoTokenizer.from_pretrained(adapter_path)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_path)

Within this repository, the adapter can be evaluated with:

bash

python distillation/evaluate_student.py \
  --input-corpus corpus/GRE/GRE_essays.json corpus/persuade/persuade_48.json \
  --base-model unsloth/SmolLM2-360M-Instruct \
  --adapter-path distillation/checkpoints/smollm2-360m-unsloth-essay-scoring-finetune \
  --output results/student_distilled_unsloth_results.json \
  --model-label smollm2-360m-unsloth-essay-scoring-finetune

Limitations

This adapter was trained for a specific essay-grading prompt format used in this repo.
Reported metrics are local evaluation results on the repository's GRE and Persuade subsets, not broad benchmark claims.
The adapter does not include the full base model weights.