122 lines
3.9 KiB
Markdown
122 lines
3.9 KiB
Markdown
---
|
|
library_name: transformers
|
|
tags:
|
|
- sft
|
|
- unsloth
|
|
- science
|
|
- reasoning
|
|
license: apache-2.0
|
|
datasets:
|
|
- mattwesney/CoT_Reasoning_Scientific_Discovery_and_Research
|
|
language:
|
|
- en
|
|
base_model:
|
|
- unsloth/Qwen3-1.7B
|
|
pipeline_tag: text-generation
|
|
---
|
|
|
|
|
|
|
|
# Model Card for Qwen3-CoT-Scientific-Research
|
|
|
|
## Model Details
|
|
|
|
### Model Description
|
|
|
|
- **Base Model:** Qwen3-1.7B
|
|
- **Task:** Scientific Reasoning with Chain-of-Thought (CoT)
|
|
- **Dataset:** CoT_Reasoning_Scientific_Discovery_and_Research (custom dataset focusing on step-by-step scientific reasoning tasks)
|
|
- **Training Objective:** Encourage step-by-step logical deductions for scientific reasoning problems
|
|
|
|
## Uses
|
|
|
|
### Direct Use
|
|
|
|
This fine-tuned model is designed for:
|
|
|
|
- Assisting in teaching and learning scientific reasoning
|
|
- Supporting educational AI assistants in science classrooms
|
|
- Demonstrating step-by-step scientific reasoning in research training contexts
|
|
- Serving as a resource for automated reasoning systems to better emulate structured scientific logic
|
|
|
|
It is not intended to replace human researchers, perform advanced analytics, or generate novel scientific discoveries.
|
|
|
|
|
|
## Bias, Risks, and Limitations
|
|
|
|
- May oversimplify complex or interdisciplinary problems
|
|
- Performance limited by the scope of training data (primarily introductory-level scientific reasoning tasks)
|
|
- Does not handle real-world experimentation or advanced statistical modeling
|
|
- May produce incorrect reasoning if the prompt is highly ambiguous
|
|
|
|
|
|
## How to Get Started with the Model
|
|
|
|
Use the code below to get started with the model.
|
|
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("khazarai/Scie-R1")
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
"khazarai/Scie-R1",
|
|
device_map={"": 0}
|
|
)
|
|
|
|
question = """
|
|
How are microfluidic devices revolutionizing laboratory analysis techniques, and what are the primary advantages they offer over traditional methods?
|
|
"""
|
|
|
|
messages = [
|
|
{"role" : "user", "content" : question}
|
|
]
|
|
text = tokenizer.apply_chat_template(
|
|
messages,
|
|
tokenize = False,
|
|
add_generation_prompt = True,
|
|
enable_thinking = True,
|
|
)
|
|
|
|
from transformers import TextStreamer
|
|
_ = model.generate(
|
|
**tokenizer(text, return_tensors = "pt").to("cuda"),
|
|
max_new_tokens = 1800,
|
|
temperature = 0.6,
|
|
top_p = 0.95,
|
|
top_k = 20,
|
|
streamer = TextStreamer(tokenizer, skip_prompt = True),
|
|
)
|
|
```
|
|
|
|
## Training Details
|
|
|
|
### Training Data
|
|
|
|
**Scope**
|
|
|
|
This model was fine-tuned on tasks that involve core scientific reasoning:
|
|
|
|
- Formulating testable hypotheses
|
|
- Identifying independent and dependent variables
|
|
- Designing simple controlled experiments
|
|
- Interpreting graphs, tables, and basic data representations
|
|
- Understanding relationships between evidence and conclusions
|
|
- Recognizing simple logical fallacies in scientific arguments
|
|
|
|
**Illustrative Examples**
|
|
|
|
- Drawing conclusions from experimental results
|
|
- Evaluating alternative explanations for observed data
|
|
- Explaining step-by-step reasoning behind scientific conclusions
|
|
|
|
**Emphasis on Chain-of-Thought (CoT)**
|
|
|
|
- The dataset highlights explicit reasoning steps, making the model better at producing step-by-step explanations when solving scientific reasoning tasks.
|
|
- Focus on Foundational Knowledge
|
|
- The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.
|
|
|
|
**Focus on Foundational Knowledge**
|
|
|
|
The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.
|
|
|
|
**Dataset:** [moremilk/CoT_Reasoning_Scientific_Discovery_and_Research](https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research) |