khazarai
/

Chemistry-R1

@@ -1,202 +1,97 @@
 ---
 base_model: unsloth/Qwen3-0.6B
 library_name: peft
 ---
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 ### Framework versions
 - PEFT 0.15.2

 ---
 base_model: unsloth/Qwen3-0.6B
 library_name: peft
+license: mit
+datasets:
+- camel-ai/loong
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- trl
+- unsloth
+- sft
+- transformers
 ---
 # Model Card for Model ID
 ## Model Details
+**Name:** Chemistry-R1
+**Base Model:** Qwen3-0.6B
+**Fine-Tuning Dataset:** ~2,000 chemistry reasoning problems, where solutions are computed step-by-step using Python code.
+**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.
+- Capabilities:
+ - Solves quantitative chemistry problems using code-based reasoning.
+ - Generates intermediate steps to explain calculations and chemical logic.
+ - Can output results as numerical answers, chemical equations, or calculated values.
 ## Uses
 ### Direct Use
+This model is designed for:
+- Educational Assistance: Helping students and educators solve and explain chemistry problems programmatically.
+- Chemistry Problem Solving: Generating step-by-step solutions for quantitative chemistry calculations.
+- Automated Reasoning Pipelines: Integrating into applications where chemistry computations need algorithmic precision.
 ## Bias, Risks, and Limitations
+- Numerical Precision: The model may occasionally produce incorrect numerical results due to floating-point approximations or coding logic errors. Always verify critical calculations.
+- 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.
+- 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.
+- No Safety Checks: It does not account for chemical hazards, experimental safety, or lab protocols—only theoretical reasoning.
+- Limited Generalization: Performance may degrade on problems requiring multi-step reasoning beyond the patterns seen in the fine-tuning dataset.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from huggingface_hub import login
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen3-0.6B",
+    device_map={"": 0}
+)
+model = PeftModel.from_pretrained(base_model,"khazarai/Chemistry-R1")
+question = """
+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,
+what is the probability that exactly two will be red? Express your answer as a common fraction
+"""
+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 = 1500,
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
 ### Framework versions
 - PEFT 0.15.2