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Model: igmoiiz/ResearchMate-Qwen2.5-3B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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tags:
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- research
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- scientific-literature
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- question-answering
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- summarization
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- instruction-tuning
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- qlora
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- unsloth
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- peft
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- lora
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- qwen2.5
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- arxiv
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- pubmed
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- academic-assistant
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library_name: transformers
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pipeline_tag: text-generation
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datasets:
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- qiaojin/PubMedQA
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- arXiv
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model-index:
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- name: ResearchMate-Qwen2.5-3B
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results: []
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---
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# ResearchMate-Qwen2.5-3B
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ResearchMate-Qwen2.5-3B is a domain-specialized Large Language Model designed to assist researchers, students, and practitioners with scientific literature.
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The model is instruction fine-tuned from **Qwen2.5-3B-Instruct** using **QLoRA** and **Unsloth**. Instead of functioning as a general-purpose chatbot, ResearchMate focuses on understanding scientific papers and responding to research-oriented instructions.
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This release represents **Version 1** of the ResearchMate project.
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---
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# Model Overview
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| Property | Value |
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|-----------|-------|
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| Model | Qwen2.5-3B-Instruct |
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| Fine-tuning | QLoRA |
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| Framework | Unsloth |
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| Parameter Count | 3B |
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| Quantization | 4-bit |
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| PEFT | LoRA |
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| Primary Domain | Scientific Literature |
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| Language | English |
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| Version | 1.0 |
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|
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---
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# Project Goal
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ResearchMate aims to provide a lightweight, open-source scientific assistant capable of understanding research papers and responding to academic instructions.
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The objective is not to replace Retrieval-Augmented Generation systems or search engines, but to improve a language model's understanding of scientific writing through supervised instruction fine-tuning.
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Version 1 focuses on instruction tuning without retrieval.
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|
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---
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# Supported Tasks
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ResearchMate has been instruction-tuned for several scientific literature tasks including:
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- Scientific Question Answering
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- Paper Summarization
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- Abstract Explanation
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- Beginner-Friendly Concept Explanation
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- Keyword Extraction
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- Research Field Identification
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- Scientific TL;DR Generation
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- Contribution Identification
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- Method Identification
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|
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---
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# Training Method
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The model was fine-tuned using:
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- Unsloth
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- QLoRA
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- PEFT LoRA
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- 4-bit Quantization
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Training was performed on Kaggle GPUs to reduce memory requirements while maintaining strong instruction-following capabilities.
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|
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---
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# Dataset Construction
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ResearchMate does **not** train directly on raw datasets.
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Instead, a dedicated dataset-building pipeline converts multiple scientific sources into a unified instruction dataset.
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The preprocessing pipeline is independent of model training.
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Dataset pipeline:
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```
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Scientific Dataset
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↓
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Validation
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↓
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|
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Schema Conversion
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||||
|
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↓
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|
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Cleaning
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|
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↓
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|
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Merge
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|
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↓
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JSONL Export
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↓
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Training
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```
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|
||||
---
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# Dataset Schema
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Every training example follows the same structure:
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|
||||
```json
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{
|
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"instruction": "...",
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"input": "...",
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"output": "...",
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||||
"source": "...",
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"task": "..."
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||||
}
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||||
```
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|
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This unified schema allows new scientific datasets to be added without modifying the training pipeline.
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|
||||
---
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# Data Sources
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Version 1 uses the following sources:
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|
||||
## PubMedQA
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Purpose:
|
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|
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- Scientific Question Answering
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||||
Configuration:
|
||||
|
||||
```
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pqa_labeled
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```
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Fields used:
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||||
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- Question
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- Context
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- Long Answer
|
||||
|
||||
---
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|
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## arXiv
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Scientific papers collected through the official arXiv API.
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Paper metadata including titles, abstracts and categories were converted into instruction-response pairs for research-oriented tasks.
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|
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---
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# Training Configuration
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| Parameter | Value |
|
||||
|-----------|-------|
|
||||
| Fine-tuning Method | QLoRA |
|
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| PEFT | LoRA |
|
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| Quantization | 4-bit |
|
||||
| Framework | Unsloth |
|
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| Optimizer | AdamW |
|
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| Epochs | 2 |
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| Batch Size | 2 |
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| Gradient Accumulation | 4 |
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| Sequence Length | 2048 |
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|
||||
---
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||||
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# Evaluation
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The model was evaluated against the original Qwen2.5-3B-Instruct model.
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Evaluation included:
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- Scientific QA
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- Response quality inspection
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- ROUGE (where applicable)
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- Latency comparison
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- Hallucination inspection
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- Task-wise qualitative comparison
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||||
|
||||
---
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||||
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# Example Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "igmoiiz/ResearchMate-Qwen2.5-3B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map="auto"
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)
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messages = [
|
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{
|
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"role": "user",
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"content": "Explain transfer learning in simple language."
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}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256
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)
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print(
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tokenizer.decode(
|
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
|
||||
)
|
||||
```
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||||
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||||
---
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||||
|
||||
# Example Prompts
|
||||
|
||||
### Scientific Question Answering
|
||||
|
||||
```
|
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Answer the scientific question using the provided context.
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||||
Question:
|
||||
What is transfer learning?
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Summarization
|
||||
|
||||
```
|
||||
Summarize the following scientific abstract.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Beginner Explanation
|
||||
|
||||
```
|
||||
Explain this abstract in simple language.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Keyword Extraction
|
||||
|
||||
```
|
||||
Extract the important keywords from this paper.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Research Field Identification
|
||||
|
||||
```
|
||||
Identify the research field of this paper.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Intended Uses
|
||||
|
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Suitable for:
|
||||
|
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- Research assistants
|
||||
- Literature exploration
|
||||
- Educational tools
|
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- Scientific tutoring
|
||||
- Academic chatbots
|
||||
- Research paper preprocessing
|
||||
- Scientific writing assistance
|
||||
|
||||
---
|
||||
|
||||
# Limitations
|
||||
|
||||
ResearchMate Version 1 has several limitations.
|
||||
|
||||
- No Retrieval-Augmented Generation (RAG)
|
||||
- No citation verification
|
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- No PDF parsing
|
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- No web search
|
||||
- No factual verification beyond the model's learned parameters
|
||||
- Performance depends on the quality and diversity of the instruction dataset
|
||||
- May generate incorrect or outdated scientific information
|
||||
|
||||
Users should verify important scientific claims using authoritative sources.
|
||||
|
||||
---
|
||||
|
||||
# Future Work
|
||||
|
||||
Planned improvements include:
|
||||
|
||||
- Retrieval-Augmented Generation (RAG)
|
||||
- Citation-aware responses
|
||||
- PDF ingestion
|
||||
- Larger scientific datasets
|
||||
- Multi-turn research conversations
|
||||
- Paper recommendation
|
||||
- Local deployment using Ollama and vLLM
|
||||
- Improved evaluation benchmarks
|
||||
- Expanded scientific domains
|
||||
|
||||
---
|
||||
|
||||
# Repository Structure
|
||||
|
||||
```
|
||||
Notebook 1
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||||
Dataset Builder
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||||
|
||||
↓
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||||
|
||||
Notebook 2
|
||||
QLoRA Fine-tuning
|
||||
|
||||
↓
|
||||
|
||||
Notebook 3
|
||||
Inference
|
||||
|
||||
↓
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||||
|
||||
Notebook 4
|
||||
Evaluation
|
||||
```
|
||||
|
||||
---
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||||
|
||||
# Citation
|
||||
|
||||
If you use this model in your work, please cite the repository.
|
||||
|
||||
```
|
||||
@software{researchmate2026,
|
||||
title={ResearchMate-Qwen2.5-3B},
|
||||
author={Moiz Baloch},
|
||||
year={2026},
|
||||
publisher={Hugging Face},
|
||||
url={https://huggingface.co/igmoiiz/ResearchMate-Qwen2.5-3B}
|
||||
}
|
||||
```
|
||||
|
||||
---
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||||
|
||||
# Acknowledgements
|
||||
|
||||
This project builds upon the work of:
|
||||
|
||||
- Alibaba Qwen Team
|
||||
- Unsloth AI
|
||||
- Hugging Face
|
||||
- PubMedQA
|
||||
- arXiv
|
||||
|
||||
Their open-source contributions made this project possible.
|
||||
|
||||
---
|
||||
|
||||
# Contact
|
||||
|
||||
Author: **Moiz Baloch**
|
||||
|
||||
GitHub: https://github.com/igmoiiz
|
||||
|
||||
Hugging Face: https://huggingface.co/igmoiiz
|
||||
|
||||
---
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||||
54
chat_template.jinja
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54
chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\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>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\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" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
70
config.json
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70
config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"torch_dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
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193
tokenizer_config.json
Normal file
193
tokenizer_config.json
Normal file
@@ -0,0 +1,193 @@
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Reference in New Issue
Block a user