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Model: ptvnck/qwen2.5-1.5b-exam-tutor Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: unsloth/Qwen2.5-1.5B-Instruct
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language:
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- en
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- ru
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen2
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- unsloth
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- trl
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- sft
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- lora
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- education
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- tutoring
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- conversational
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datasets:
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- ptvnck/TutoringDialogs
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- eth-nlped/mathdial
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model-index:
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- name: qwen2.5-1.5b-exam-tutor
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results: []
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---
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<div align="center">
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<span style="font-size:44px; font-weight:bold">Qwen2.5-1.5B Exam Tutor</span>
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**Tutoring assistant for preparing to exams, fine-tuned to help you *think*, not just get answers.**
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[](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
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[](https://www.apache.org/licenses/LICENSE-2.0)
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[](https://github.com/unslothai/unsloth)
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[]()
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</div>
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---
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## Overview
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`qwen2.5-1.5b-exam-tutor` is a LoRA fine-tune of [`Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/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.
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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.
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> 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](#-limitations--scope) below.
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## Model Details
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| | |
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|---|---|
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| **Base model** | [`unsloth/Qwen2.5-1.5B-Instruct`](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) |
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| **Fine-tuning method** | LoRA (rank 16), full precision (no 4-bit quantization) |
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| **Frameworks** | [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) `SFTTrainer` |
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| **Weights format** | Merged 16-bit safetensors (adapter merged into base) |
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| **Language** | English / Russian |
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| **License** | Apache 2.0 (inherited from base model) |
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## Intended Use
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- Conversational tutoring for exam preparation: math word problems, conceptual explanations, step-by-step reasoning practice.
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- Designed to be embedded as the generation backend of a larger RAG + FastAPI tutoring assistant (see [Roadmap](#-roadmap)).
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- **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.
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## Training Data
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650 student ↔ tutor dialogues, combined from two sources:
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| Source | Dialogues used | Notes |
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|---|---|---|
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| [`ptvnck/TutoringDialogs`](https://huggingface.co/datasets/ptvnck/TutoringDialogs) | 500 | Synthetically generated, manually curated tutoring dialogues across mixed subjects |
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| [`eth-nlped/mathdial`](https://huggingface.co/datasets/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 |
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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).
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## Training Procedure
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<details>
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<summary><b>LoRA configuration</b></summary>
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| Parameter | Value |
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|---|---|
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| Rank (`r`) | 16 |
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| Alpha (`lora_alpha`) | 32 |
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| Target modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
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| Dropout | 0.1 |
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| Bias | none |
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| Gradient checkpointing | Unsloth-optimized |
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</details>
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<details>
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<summary><b>Optimization hyperparameters</b></summary>
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| Parameter | Value |
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|---|---|
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| Effective batch size | 12 (4 × grad. accumulation 3) |
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| Epochs | 4 (best checkpoint auto-selected) |
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| Learning rate | 2e-4, cosine schedule |
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| Warmup | 10% of total steps |
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| Optimizer | AdamW (torch) |
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| Precision | fp16 |
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| Loss masking | Response-only (`train_on_responses_only`) — loss computed exclusively on tutor turns |
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| Hardware | 1× NVIDIA T4 (Google Colab) |
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</details>
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**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.
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## Results
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| Epoch | Training Loss | Validation Loss |
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|---|---|---|
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| 1 | 1.655 | 1.724 |
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| 2 | 1.389 | 1.516 |
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| **3** | **1.050** | **1.506** ← best |
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| 4 | 0.703 | 1.580 |
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- **Best validation loss:** 1.506 (epoch 3) → **perplexity ≈ 4.51**
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- 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).
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- `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.
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Full training curves (loss, LR schedule) were tracked with Weights & Biases.
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## How to Use
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**With 🤗 Transformers:**
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("ptvnck/qwen2.5-1.5b-exam-tutor")
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model = AutoModelForCausalLM.from_pretrained("ptvnck/qwen2.5-1.5b-exam-tutor")
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messages = [
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{"role": "user", "content": "I need to solve 2x + 5 = 15 but I don't know where to start."}
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]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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**With Unsloth (2x faster inference):**
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="ptvnck/qwen2.5-1.5b-exam-tutor",
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max_seq_length=2048,
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)
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FastLanguageModel.for_inference(model)
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```
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**With vLLM:**
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```bash
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pip install vllm
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vllm serve "ptvnck/qwen2.5-1.5b-exam-tutor"
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```
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## Limitations & Scope
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- 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.
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- 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.
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- Not safety-tuned beyond what the base `Qwen2.5-1.5B-Instruct` already provides.
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## Acknowledgements
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- Base model: [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) by the Qwen team
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- Training accelerated with [Unsloth](https://github.com/unslothai/unsloth)
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- Trained using Hugging Face [TRL](https://github.com/huggingface/trl)
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- `mathdial` subset: [eth-nlped/mathdial](https://huggingface.co/datasets/eth-nlped/mathdial)
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
|
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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62
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": 151654,
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"rms_norm_eps": 1e-06,
|
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"rope_parameters": {
|
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"rope_theta": 1000000.0,
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"rope_type": "default"
|
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},
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"sliding_window": null,
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||||||
|
"tie_word_embeddings": true,
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"unsloth_version": "2026.7.2",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
{
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"max_length": 32768,
|
||||||
|
"pad_token_id": 151654,
|
||||||
|
"repetition_penalty": 1.1,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.8,
|
||||||
|
"transformers_version": "5.5.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6debc3376278ed2e7151e9957304321e895a849bcf9f63f6d0d0622bf5323491
|
||||||
|
size 3087467144
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6b4360dd6a184650ffc48056c2569bc603f896c5adfe94b10f1c79f809638aa5
|
||||||
|
size 11422166
|
||||||
194
tokenizer_config.json
Normal file
194
tokenizer_config.json
Normal file
@@ -0,0 +1,194 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": false,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|vision_pad|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user