Update README.md

README.md

@@ -1,199 +1,124 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- sft
+- unsloth
+- science
+- reasoning
+license: apache-2.0
+datasets:
+- mattwesney/CoT_Reasoning_Scientific_Discovery_and_Research
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Qwen3-CoT-Scientific-Research
 
 
+![20250421_1023_Scientific Discovery Design_simple_compose_01jscbjwqdetvtd5phh85mwsa7.png](https://cdn-uploads.huggingface.co/production/uploads/65dbedfd2f6d2dfc27763b98/mXuepdf8ZDBDMtdaBx-nV.png)
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This fine-tuned model is designed for:
 
-### Downstream Use [optional]
+ - 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
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+It is not intended to replace human researchers, perform advanced analytics, or generate novel scientific discoveries.
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- 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
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```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
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
+**Scope**
 
-[More Information Needed]
+This model was fine-tuned on tasks that involve core scientific reasoning:
 
-## Glossary [optional]
+- 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
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+**Illustrative Examples**
 
-[More Information Needed]
+- Drawing conclusions from experimental results
+- Evaluating alternative explanations for observed data
+- Explaining step-by-step reasoning behind scientific conclusions
 
-## More Information [optional]
+**Emphasis on Chain-of-Thought (CoT)**
 
-[More Information Needed]
+- 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.
 
-## Model Card Authors [optional]
+**Focus on Foundational Knowledge**
 
-[More Information Needed]
+The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.
 
-## Model Card Contact
+**Dataset:** [moremilk/CoT_Reasoning_Scientific_Discovery_and_Research](https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research)
 
-[More Information Needed]