qwen2.5-1.5b-exam-tutor/README.md

license, base_model, language, library_name, pipeline_tag, tags, datasets, model-index

license	base_model	language	library_name	pipeline_tag	tags	datasets	model-index
apache-2.0	unsloth/Qwen2.5-1.5B-Instruct	en ru	transformers	text-generation	qwen2 unsloth trl sft lora education tutoring conversational	ptvnck/TutoringDialogs eth-nlped/mathdial	name results qwen2.5-1.5b-exam-tutor

Qwen2.5-1.5B Exam Tutor

Tutoring assistant for preparing to exams, fine-tuned to help you think, not just get answers.

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Overview

qwen2.5-1.5b-exam-tutor is a LoRA fine-tune of Qwen2.5-1.5B-Instruct, trained to behave like a patient human tutor rather than an answer-dispensing machine. Instead of solving a problem outright, the model is trained to ask guiding questions, probe for misconceptions, and walk the student toward the solution themselves — the same pattern a good teacher uses during exam prep.

This model is the first stage of a larger personal-assistant project for exam preparation, which also includes a RAG pipeline over practice problems and a FastAPI serving layer accelerated with vLLM/Ollama.

This is an educational / portfolio project, not a production system. Scope, dataset size, and evaluation depth are intentionally sized for a learning exercise — see Limitations below.

Model Details

Base model	unsloth/Qwen2.5-1.5B-Instruct
Fine-tuning method	LoRA (rank 16), full precision (no 4-bit quantization)
Frameworks	Unsloth + TRL SFTTrainer
Weights format	Merged 16-bit safetensors (adapter merged into base)
Language	English / Russian
License	Apache 2.0 (inherited from base model)

Intended Use

• Conversational tutoring for exam preparation: math word problems, conceptual explanations, step-by-step reasoning practice.
• Designed to be embedded as the generation backend of a larger RAG + FastAPI tutoring assistant (see Roadmap).
• Not intended as a general-purpose assistant, factual knowledge base, or replacement for a real teacher — the model's job is to guide, its factual accuracy on niche topics is not separately verified.

Training Data

650 student ↔ tutor dialogues, combined from two sources:

Source	Dialogues used	Notes
ptvnck/TutoringDialogs	500	Synthetically generated, manually curated tutoring dialogues across mixed subjects
eth-nlped/mathdial	150	Filtered (dialogues with >11 turns) and reformatted subset, added specifically to cover math word-problem tutoring, which was underrepresented in the primary dataset

Data was split 85/15 into train/validation (≈552 / 98 examples), formatted with the tokenizer's ChatML template, and capped at 2500 tokens (covering the 99th percentile of dialogue length with no truncation).

Training Procedure

LoRA configuration

Parameter	Value
Rank (r)	16
Alpha (lora_alpha)	32
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Dropout	0.1
Bias	none
Gradient checkpointing	Unsloth-optimized

Optimization hyperparameters

Parameter	Value
Effective batch size	12 (4 × grad. accumulation 3)
Epochs	4 (best checkpoint auto-selected)
Learning rate	2e-4, cosine schedule
Warmup	10% of total steps
Optimizer	AdamW (torch)
Precision	fp16
Loss masking	Response-only (train_on_responses_only) — loss computed exclusively on tutor turns
Hardware	1× NVIDIA T4 (Google Colab)

Response-only loss masking. Only the tutor's turns contribute to the training loss; the student's turns are masked out. This keeps the adapter's limited capacity focused entirely on learning how to tutor, rather than also learning to imitate the student side of the conversation.

Results

Epoch	Training Loss	Validation Loss
1	1.655	1.724
2	1.389	1.516
3	1.050	1.506 ← best
4	0.703	1.580

• Best validation loss: 1.506 (epoch 3) → perplexity ≈ 4.51
• Training loss keeps decreasing through epoch 4, while validation loss starts rising after epoch 3 — a clear sign of overfitting setting in on the final epoch, expected given the modest dataset size (~550 training examples).
• load_best_model_at_end=True automatically restored the epoch-3 checkpoint as the final model, so the released weights are not the last-epoch weights, but the best-validation checkpoint.

Full training curves (loss, LR schedule) were tracked with Weights & Biases.

How to Use

With 🤗 Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ptvnck/qwen2.5-1.5b-exam-tutor")
model = AutoModelForCausalLM.from_pretrained("ptvnck/qwen2.5-1.5b-exam-tutor")

messages = [
    {"role": "user", "content": "I need to solve 2x + 5 = 15 but I don't know where to start."}
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

With Unsloth (2x faster inference):

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="ptvnck/qwen2.5-1.5b-exam-tutor",
    max_seq_length=2048,
)
FastLanguageModel.for_inference(model)

With vLLM:

pip install vllm
vllm serve "ptvnck/qwen2.5-1.5b-exam-tutor"

Limitations & Scope

• Trained on 650 dialogues — sufficient to learn a tutoring pattern, but not a broad knowledge base. Expect a strong grasp of conversational tutoring style, and a shallower grasp of niche subject-matter facts.
• No dedicated generation-quality evaluation (human eval / LLM-as-judge) was run as part of this stage — this is deferred to the RAG + FastAPI integration stage of the project, where end-to-end assistant responses will be evaluated in context rather than in isolation.
• Not safety-tuned beyond what the base Qwen2.5-1.5B-Instruct already provides.

Acknowledgements

• Base model: Qwen2.5-1.5B-Instruct by the Qwen team
• Training accelerated with Unsloth
• Trained using Hugging Face TRL
• mathdial subset: eth-nlped/mathdial