uae-adab-tutor-qwen3-4b

UAE Adab Tutor — Qwen3-4B

This repository contains the selected Complete-600 LoRA adapter for Qwen/Qwen3-4B-Instruct-2507. The base is pinned to revision cdbee75f17c01a7cc42f958dc650907174af0554.

Release version: exact-silver v1 Complete-600.

Behavior spec

Across a pressured multi-turn lesson, the tutor should teach the academic content accurately, correct the specific work without humiliating the learner, protect learner authorship and assessment integrity, allow respectful evidence-based disagreement with adults, avoid religious rulings or sectarian claims, and continue useful tutoring without drifting from that method.

This is a narrow behavior model. It is not intended to make Qwen generally smarter or to replace a teacher, parent, or religious authority.

Primary result

The frozen primary evaluation used ten five-turn scenario clusters, for 50 judged turns per condition. The same pinned Qwen base, scenarios, native chat template, deterministic decoding, 4,096-token context, and blind Claude Sonnet 4.6 judge were used for both Qwen deployments.

The tuned condition used this adapter and the fixed system token <uae_adab_tutor>default</uae_adab_tutor>. The base condition used a substantial frozen behavioral prompt. This is therefore a comparison of two complete deployments, not a pure adapter-only causal estimate.

A saved GPT-5.6 Luna strong-prompt run scored 9.44/10 and 80% strict turns when rescored by the same blind judge. Luna used different provider decoding and a larger answer budget, so that result is supplementary. The 4B model did not beat the prompted frontier overall. No formal Claude answer-model benchmark was run; any live Claude comparison in the demo is illustrative only.

Training dataset

The public dataset is adarshrajesh/uae-adab-tutor-600.

The grounded rows incorporate source-specific academic substance or ordered teaching moves. They are adapted, model-written conversations, not verbatim gold transcripts. Fresh exact-case fidelity review passed 111 of the 120 grounded rows. The remaining rows and all provisional tiers are disclosed in the dataset metadata and limitation ledger.

Training configuration

Validation loss fell from 2.1964 at step 10 to 1.7373 at step 70 in the saved run. Behavioral evaluation, rather than loss alone, determined the selected model.

Inference contract

Use the native Qwen chat template, disable thinking, and supply the fixed control token as the system message:

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

base_id = "Qwen/Qwen3-4B-Instruct-2507"
base_revision = "cdbee75f17c01a7cc42f958dc650907174af0554"
adapter_id = "adarshrajesh/uae-adab-tutor-qwen3-4b"

tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)
compute_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
quantization = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=compute_dtype,
)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    revision=base_revision,
    torch_dtype=compute_dtype,
    quantization_config=quantization,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_id).eval()

messages = [
    {"role": "system", "content": "<uae_adab_tutor>default</uae_adab_tutor>"},
    {"role": "user", "content": "I think 1/8 is larger than 1/6. Is that right?"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt", truncation=False).to(model.device)
with torch.inference_mode():
    output = model.generate(**inputs, do_sample=False, max_new_tokens=512)
print(tokenizer.decode(output[0, inputs.input_ids.shape[-1]:], skip_special_tokens=True))

The recorded evaluation refused silent truncation and required the prompt plus the 512-token generation budget to fit within 4,096 tokens.

Demo and reproduction

The reviewer path uses a temporary L4 or A100 Colab session. Public entry points:

• GitHub: https://github.com/adarshrajesh-ui/uae-adab-tutor
• Temporary Colab demo: https://colab.research.google.com/github/adarshrajesh-ui/uae-adab-tutor/blob/main/submission/notebooks/03_demo_uae_adab_tutor.ipynb
• Training notebook: submission/notebooks/01_train_exact_silver_v1.ipynb
• Evaluation notebook: submission/notebooks/02_evaluate_exact_silver_v1.ipynb

For the video, the temporary Colab output is shown beside two fresh Claude windows, one question-only and one strongly prompted. That live Claude view is not presented as a measured benchmark.

Limitations and use boundary

• This is experimental silver, not publication-grade gold.
• The primary result comes from one training seed, one deterministic generation per Qwen condition, ten scenario clusters, and one external judge.
• The selected run still made two material factual errors in 50 judged turns.
• The separate source-transfer and authentic held-out suites were not run.
• This model is not approved as a child-facing production service and is not a religious authority.
• The dataset is mixed-rights and noncommercial. The 21 Arabic-video cases rely on the project owner's permission attestation for nonverbatim derivation and redistribution. Confirm the permission scope before distributing weights for another use.

Frozen adapter identity

Original two-file directory fingerprint: c98e6ebda3e2bd8f6b885a318f8987035547477f796717c76d4846824df20ce0.