abcorrea/sched-v4
abcorrea/sched-v4 is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Thinking-2507, designed for general text generation tasks. This model leverages a 40960-token context length, making it suitable for processing and generating longer sequences of text. It was trained using the TRL framework, focusing on instruction-following capabilities.
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Model Overview
abcorrea/sched-v4 is a 4 billion parameter language model, fine-tuned from the base model Qwen/Qwen3-4B-Thinking-2507. This model is designed for general text generation and instruction-following tasks, benefiting from its substantial 40960-token context window.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: Fine-tuned to understand and respond to user instructions, making it suitable for conversational agents or task-oriented applications.
- Extended Context Handling: With a 40960-token context length, it can process and maintain understanding over longer input sequences, which is beneficial for complex queries or multi-turn conversations.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library. This approach helps in aligning the model's outputs with human preferences and instructions. The training utilized specific versions of key frameworks:
- TRL: 0.27.1
- Transformers: 5.0.0
- Pytorch: 2.7.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
When to Use This Model
abcorrea/sched-v4 is a good choice for applications requiring a compact yet capable model for:
- Generating creative content or responses.
- Handling prompts that require understanding of long contexts.
- Developing chatbots or virtual assistants that need to follow specific instructions.