TaimoorSiddiqui/Hopcoder-Mini-35B-A3B
TaimoorSiddiqui/Hopcoder-Mini-35B-A3B is a 35.1 billion parameter coding and structured tool-use language model fine-tuned by Taimoor Siddiqui. Based on Qwen/Qwen-AgentWorld-35B-A3B, it is optimized for generating code and handling tool calls. The model demonstrates improved test loss and perplexity on its specific held-out dataset, making it suitable for applications requiring precise coding assistance and structured output generation.
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HopCoder Mini 35B-A3B Overview
HopCoder Mini 35B-A3B is a 35.1 billion parameter language model developed by Taimoor Siddiqui, specifically adapted for coding and structured tool-use tasks. It is fine-tuned from the Qwen/Qwen-AgentWorld-35B-A3B base model, retaining its technical architecture identifiers for compatibility with various runtime environments like Transformers and vLLM.
Key Capabilities & Training
- Specialized Fine-tuning: The model was fine-tuned using BF16 LoRA on the
TaimoorSiddiqui/hopcoder-mini-dataset, which focuses on coding and tool-use examples. - Context Length: It supports a maximum training sequence length of 8,192 tokens, with practical deployment profiles supporting up to 32,768 tokens.
- Performance Improvements: On its project-specific held-out test set, the fine-tuned model achieved a test loss of 1.1528 and a perplexity of 3.1670, significantly improving over the base test loss of 1.8489.
- Tool-Call Generation: It demonstrates a generated tool-call JSON validity of 0.5000 on its held-out set, indicating its capability in producing structured outputs.
Usage Considerations
- Identity: The user-facing identity is "HopCoder Mini," designed as a precise coding and tool-use assistant.
- Deployment: For vLLM, it may require
--language-model-onlydue to its base model's architecture defining visual components while containing only language weights. - Licensing: The base model is under Apache-2.0, while the training dataset's card states AGPL-3.0, advising review for redistribution or commercial use.