TaimoorSiddiqui/HopCoder-Mini-35B-A3B-VL36-fullsft
The TaimoorSiddiqui/HopCoder-Mini-35B-A3B-VL36-fullsft is an endpoint-ready, full-SFT checkpoint based on the Qwen-family architecture, featuring packed MoE expert tensors. This model is specifically trained for tool calling, with a defined JSON format for tool interactions. It is optimized for deployment environments like Featherless, supporting up to 16k context length for prompts and completions.
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Model Overview
The TaimoorSiddiqui/HopCoder-Mini-35B-A3B-VL36-fullsft is a fully instruction-tuned (SFT) checkpoint, designed for immediate deployment. It utilizes a Qwen-family architecture with packed Mixture-of-Experts (MoE) tensors, making it efficient for serving. Users should ensure they are running Transformers version >=5.5.0 for proper qwen3_5_moe support.
Key Capabilities
- Tool Calling: The model is explicitly trained for tool calling, using a structured JSON format within
<tool_call>tags. This enables seamless integration with external functions and APIs. - Deployment Ready: Provided as a full model weight (not LoRA or QLoRA), with safetensors shards and
model.safetensors.index.json, making it compatible with platforms like Featherless. - FP16 Export: The model weights are exported in FP16 dtype for optimized performance and reduced memory footprint.
- Context Length: While the native configuration may support larger contexts, it is optimized for up to 16k context length for prompts and completions in deployment environments like Featherless.
Usage Notes
After a <tool_response> turn, clients are expected to continue generation until the model provides a final answer, indicating the completion of the tool-assisted task.