qrk-labs/akeel-cot-qwen3-4B-3k-v2b
The qrk-labs/akeel-cot-qwen3-4B-3k-v2b is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B, utilizing the TRL framework. This model is optimized for general text generation tasks, leveraging its Qwen3 architecture and a 32k context length. It is designed for applications requiring a capable and efficient language model for diverse conversational and generative prompts.
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Overview
The qrk-labs/akeel-cot-qwen3-4B-3k-v2b is a 4 billion parameter language model, fine-tuned from the Qwen/Qwen3-4B base model. This iteration, developed by qrk-labs, leverages the TRL (Transformers Reinforcement Learning) framework for its training process, specifically using Supervised Fine-Tuning (SFT).
Key Capabilities
- Base Model: Built upon the robust Qwen3-4B architecture.
- Fine-Tuning: Utilizes the TRL library for supervised fine-tuning, enhancing its performance for general text generation.
- Context Length: Supports a context length of 32,768 tokens, allowing for processing and generating longer sequences of text.
- General Purpose: Designed to handle a wide array of text generation tasks, as demonstrated by its quick start example for open-ended questions.
Training Details
The model was trained using Supervised Fine-Tuning (SFT). The development environment included specific versions of key frameworks:
- TRL: 0.28.0
- Transformers: 5.2.0
- Pytorch: 2.10.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
Good For
- General Text Generation: Suitable for various generative AI applications, including answering questions, creative writing, and conversational AI.
- Experimentation: Provides a solid base for further fine-tuning or research, given its clear lineage and training methodology.