初始化项目，由ModelHub XC社区提供模型

Model: khazarai/Scie-R1
Source: Original Platform

README.md

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+---
+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
+---
+
+![20250421_1023_Scientific Discovery Design_simple_compose_01jscbjwqdetvtd5phh85mwsa7.png](https://cdn-uploads.huggingface.co/production/uploads/65dbedfd2f6d2dfc27763b98/mXuepdf8ZDBDMtdaBx-nV.png)
+
+# 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)