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Model: rizkysulaeman/qwen_2_3B_reasoning_en_ft_v1
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- qwen2
+- reasoning
+- fine-tuned
+- english
+- instruction-tuned
+- chat
+- gguf
+metrics:
+- accuracy
+- f1
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- openai/gsm8k
+---
+
+# Model Card for CALISTA-INDUSTRY/qwen_2_3B_reasoning_en_ft_v1
+
+## Model Details
+
+- **Model Name**: qwen_2_3B_reasoning_en_ft_v1
+- **Developed by**: Mohammad Yani & Rizky Sulaeman, Politeknik Negeri Indramayu
+- **Model Type**: Transformer-based language model
+- **Base Model**: [Qwen/Qwen2-3B](https://huggingface.co/Qwen/Qwen2-3B)
+- **Parameter Count**: 3.09 billion
+- **Language**: English
+- **License**: Apache 2.0
+- **Fine-tuned from**: Qwen2-3B
+
+## Model Description
+
+This model is a fine-tuned version of Qwen2-3B, optimized for enhanced reasoning capabilities in English. It has been trained on a curated dataset to improve performance on tasks requiring logical inference, comprehension, and instruction following.
+
+## Intended Uses & Limitations
+
+### Direct Use
+
+- **Applications**:
+  - Logical reasoning tasks
+  - Instruction-based question answering
+  - Conversational agents requiring enhanced reasoning
+
+### Downstream Use
+
+- **Potential Applications**:
+  - Integration into AI systems requiring reasoning capabilities
+  - Further fine-tuning for domain-specific tasks
+
+### Out-of-Scope Use
+
+- **Not Recommended For**:
+  - Tasks requiring real-time decision-making in critical systems
+  - Applications involving sensitive or personal data without proper safeguards
+
+## Bias, Risks, and Limitations
+
+While efforts have been made to reduce biases during fine-tuning, the model may still exhibit biases present in the training data. Users should be cautious and evaluate the model's outputs, especially in sensitive applications.
+
+## How to Use
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="CALISTA-INDUSTRY/qwen_2_3B_reasoning_en_ft_v1")
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe(messages)
+```
+