language, license, tags, metrics, base_model, library_name, pipeline_tag, datasets

language	license	tags	metrics	base_model	library_name	pipeline_tag	datasets
en	apache-2.0	qwen2 reasoning fine-tuned english instruction-tuned chat gguf	accuracy f1	Qwen/Qwen2.5-3B-Instruct	transformers	text-generation	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
• 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

# 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)