87 lines
3.1 KiB
Markdown
87 lines
3.1 KiB
Markdown
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---
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library_name: transformers
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tags:
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- sft
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- unsloth
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- reasoning
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license: apache-2.0
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datasets:
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- camel-ai/loong
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language:
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- en
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base_model:
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- unsloth/Qwen3-0.6B
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pipeline_tag: text-generation
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---
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# Model Card for Chemistry-R1
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## Model Details
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- **Name:** Chemistry-R1
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- **Base Model:** Qwen3-0.6B
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- **Fine-Tuning Dataset:** ~2,000 chemistry reasoning problems, where solutions are computed step-by-step using Python code.
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- **Training Objective:** The model was fine-tuned to reason through chemistry problems, generate step-by-step solutions using Python, and compute the final answer programmatically.
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- Capabilities:
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- Solves quantitative chemistry problems using code-based reasoning.
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- Generates intermediate steps to explain calculations and chemical logic.
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- Can output results as numerical answers, chemical equations, or calculated values.
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## Uses
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### Direct Use
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This model is designed for:
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- Educational Assistance: Helping students and educators solve and explain chemistry problems programmatically.
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- Chemistry Problem Solving: Generating step-by-step solutions for quantitative chemistry calculations.
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- Automated Reasoning Pipelines: Integrating into applications where chemistry computations need algorithmic precision.
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## Bias, Risks, and Limitations
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- Numerical Precision: The model may occasionally produce incorrect numerical results due to floating-point approximations or coding logic errors. Always verify critical calculations.
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- Scope of Chemistry Knowledge: Fine-tuned on ~2K problems, so it may fail on very advanced or niche chemistry topics not represented in the training set.
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- Python Execution Needed: The model generates Python code to solve problems, so it relies on a safe execution environment for computing final answers. It may not directly provide plain-text solutions without executing code.
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- No Safety Checks: It does not account for chemical hazards, experimental safety, or lab protocols—only theoretical reasoning.
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- Limited Generalization: Performance may degrade on problems requiring multi-step reasoning beyond the patterns seen in the fine-tuning dataset.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("khazarai/Chemistry-R1")
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model = AutoModelForCausalLM.from_pretrained(
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"khazarai/Chemistry-R1",
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device_map={"": 0}
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)
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question = """
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A bowl contains 10 jellybeans (four red, one blue and five white). If you pick three jellybeans from the bowl at random and without replacement,
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what is the probability that exactly two will be red? Express your answer as a common fraction
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"""
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messages = [
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{"role" : "user", "content" : question}
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]
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text = 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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enable_thinking = True,
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)
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from transformers import TextStreamer
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_ = model.generate(
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**tokenizer(text, return_tensors = "pt").to("cuda"),
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max_new_tokens = 1500,
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temperature = 0.6,
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top_p = 0.95,
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top_k = 20,
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streamer = TextStreamer(tokenizer, skip_prompt = True),
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)
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```
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