seanpoyner/smolcode-coder-powershell-1.5b-tools

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 14, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The seanpoyner/smolcode-coder-powershell-1.5b-tools is a 1.5 billion parameter LoRA fine-tune of Qwen2.5-Coder-1.5B-Instruct, developed by seanpoyner. This model is specifically trained to emit native function calls, enabling agentic coding loops for small language models. With a 32768 token context length, it excels at integrating tool use within coding assistants, particularly for PowerShell environments.

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Overview

This model, seanpoyner/smolcode-coder-powershell-1.5b-tools, is a 1.5 billion parameter LoRA fine-tune of the Qwen2.5-Coder-1.5B-Instruct base model. Its primary innovation is teaching the model to emit native <tool_call> function calls, which is crucial for enabling agentic coding loops in small language models (SLMs).

Key Capabilities

  • Native Tool Calling: Unlike standard small Qwen-Coder models that output plain-text JSON for tool calls, this fine-tune generates the native <tool_call> format, which is directly parseable by runtimes like Ollama and llama.cpp.
  • Agentic Coding: Designed to drive agentic coding assistants, specifically built for the smolcode project during the Hugging Face Build Small hackathon.
  • Optimized Training: Trained using bf16 LoRA with assistant-only loss, focusing on tool calls and final answers. The training data combined NousResearch/hermes-function-calling-v1 with synthetic smolcode tool-use trajectories, ensuring byte-identical training targets to inference prompts.

Good For

  • Developers building agentic coding assistants that require precise, native tool-call generation from small models.
  • Use cases where efficient and reliable function calling is critical for automating coding tasks.
  • Environments leveraging the Qwen2.5 chat template with tools= for structured interactions.

Finetunes
1 models