AAIE GFT Full FG 8K
moinsaj/aaie-gft-full-fg-8k is a 354M-parameter AAIE research prototype for
structured educational feedback generation. It is the standalone, full-weight
epoch-2 checkpoint from two epochs of supervised feedback-generation training.
It is not a LoRA adapter: download this repository alone to load the model.
Scope
The model was trained to generate this response shape from a complete AAIE assignment description, rubric and student submission:
{
"criterion_scores": {},
"criterion_status": {},
"criterion_feedback": {},
"overall_feedback": {}
}
The published tokenizer and configuration include a YaRN 8,192-token
setting and stop on either <|endoftext|> (151643) or <|im_end|> (151645).
This means inputs can be configured up to 8K tokens; it is not evidence of
reliable long-context retrieval or assessment.
Results and limitations
On 50 reused development records, the selected checkpoint produced 82% strict
JSON and 84% EOS completion. It also showed 100% severe repetition, only 2%
exact criterion-ID sets, and a score MAE of 2.0 wherever numeric comparison was
possible. It can therefore demonstrate a learned JSON-like feedback pattern,
but is not a reliable autonomous assessor, educator replacement, or production
Gemini replacement. The included training_run_report.json gives the exact
aggregate run evidence; it contains no student submissions or raw predictions.
Load with Transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "moinsaj/aaie-gft-full-fg-8k"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa"
)
# `fg_prompt` must follow the AAIE FG prompt contract and ends with `Response:`.
messages = [{"role": "user", "content": fg_prompt}]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
generated = model.generate(
inputs, max_new_tokens=1600, do_sample=False,
eos_token_id=[151643, 151645], pad_token_id=151643, use_cache=True,
)
answer = tokenizer.decode(generated[0, inputs.shape[1]:], skip_special_tokens=True)
print(answer)
Use deterministic decoding for reproducible diagnostic comparisons. Parse and validate the JSON before use; do not rely on the model to infer missing evidence or score an unverified student submission.
Training provenance
- Parent lineage: published AAIE GFT Llama-compatible checkpoint, revision
7dbc72d720237bd6abaaa3e7701ca69544a03e4a. - Architecture: Llama causal LM, 20 layers, hidden size 512, 8 attention heads, 2 key/value heads, vocabulary 151,936.
- Fine-tuning: full weights, FP32 parameters with BF16 autocast, AdamW,
learning rate
5e-6, 3% warm-up and cosine decay, batch 1 with accumulation 8, two epochs. - Dataset: 1,621 fitting complete training records; 261 fitting validation
records.
evidence_mapwas retained in provenance only, not as generated content. No raw records, prompts, submissions, teacher feedback or predictions are published here. - Checkpoint SHA-256:
95828700cfc649f8e2d0d75b400824e144855573f930871468cabd616b99689e.
Intended use
Research, reproducible testing and supervised demonstrations only. Human educator review is required for any feedback or assessment decision. Confirm the parent model's licence and your data-use obligations before reuse.