umwe/wandaa-v09-merged
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 15, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The umwe/wandaa-v09-merged model is a 3.1 billion parameter Qwen2.5-3B-Instruct variant, fine-tuned by umwe. It was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for efficient performance and leverages a 32768 token context length, making it suitable for applications requiring substantial context processing.
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umwe/wandaa-v09-merged: An Efficient Qwen2.5 Fine-tune
The umwe/wandaa-v09-merged model is a 3.1 billion parameter language model developed by umwe. It is a fine-tuned version of the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture.
Key Capabilities & Training
- Efficient Training: This model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Base Model: Built upon the Qwen2.5-3B-Instruct foundation, it inherits the capabilities of this instruction-tuned model.
- Context Length: Features a substantial context window of 32768 tokens, allowing for processing of longer inputs and maintaining conversational coherence over extended interactions.
Good For
- Applications requiring a compact yet capable language model.
- Scenarios where efficient inference and training are priorities.
- Tasks benefiting from a large context window, such as summarization of long documents or complex conversational agents.