Tagged "tool-calling"
25 articles tagged tool-calling, 28 February 2026 to 9 October 2026. Newest first.
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Saluki 27B: 2-bit Quantised Qwen Model Outperforms Original at Tool Calling
A new 2-bit quantised variant of Qwen3.8-27B demonstrates superior tool calling capabilities compared to the original model, showing that aggressive quantisation can preserve or improve specific task performance.
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Ollama v0.34.0: ChatGPT Desktop Integration and Apple Silicon Improvements
Ollama releases v0.34.0 with ChatGPT Desktop integration, improved structured output performance on Apple Silicon, and enhanced model management features for local deployment.
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Ollama v0.33.1 Adds Qwen3.8-Flash-Next Support via MLX Backend
Ollama's latest release includes native Qwen3.8-Flash-Next support through its MLX backend, along with structured output capabilities and Metal GPU optimizations for macOS users.
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llama.cpp Improves Muse Glimmer Tool Calling with Latest Update
The latest llama.cpp release (b10380) fixes critical tool calling behavior in Muse Glimmer models, ensuring proper handling of multiple tool invocations and preventing content swallowing issues. This update is essential for reliable agent-based local inference.
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llama.cpp Updates Tool Call Detection for Muse Glimmer
llama.cpp release b10380 fixes critical tool call detection in Muse Glimmer, addressing issues where tool invocations were being incorrectly parsed. This update improves agent reliability for local deployments using the popular inference framework.
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Ollama Releases NVIDIA Nemotron 3.5 Lightning for Agent Execution
Ollama v0.32.9 now includes NVIDIA's Nemotron 3.5 Lightning, a 30B MoE model with only 3B active parameters optimized for on-device agent execution. This lightweight model is designed for frameworks like OpenClaw and Hermes Agent, making powerful agentic AI accessible on local hardware.
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Meta's Muse Glimmer Now Available Across All Platforms via Ollama
Ollama v0.32.8 brings Meta's Muse Glimmer to all platforms with optimized support, including state-of-the-art Apple Silicon performance via MLX. Muse Glimmer powers coding agent applications and personal assistants entirely on-device.
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Liquid AI LFM2.5-2.6B: Open-Weights Agentic Model With 128K Context and Tool Calling
Liquid AI releases an open-weights agentic model optimized for on-device deployment with 128K context window, tool calling capabilities, and support for extremely low-resource edge hardware.
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Liquid AI Releases LFM2.5-2.6B: Powerful Agentic Model for Raspberry Pi and Edge Devices
Liquid AI's new LFM2.5-2.6B model brings agentic AI capabilities to resource-constrained devices like Raspberry Pi, featuring 128K context window and tool calling without requiring GPUs or cloud infrastructure.
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LFM2.5-2.6B: On-Device Agentic Model With 128K Context and Tool Calling
Detailed technical analysis of Liquid AI's LFM2.5-2.6B with open weights, demonstrating how 128K context and tool-calling capabilities are achievable in a 2.6B parameter model optimized for local inference.
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How to Build CLI Agents with Python & Ollama
A practical guide for building command-line agents using Python and Ollama, enabling local LLM-powered automation without cloud dependencies. The tutorial covers practical implementation patterns for agent development with locally-deployed models.
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7 Python Frameworks for Orchestrating Local AI Agents
KDnuggets publishes a comprehensive overview of Python frameworks for building and orchestrating AI agents that run locally. The guide covers frameworks that enable autonomous agent development without cloud dependencies, critical for privacy-sensitive and latency-critical applications.
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My Local LLM Can Call Every Tool That Claude Can, Except It Runs on My Own Hardware
A deep dive into implementing comprehensive tool-calling capabilities in locally-hosted LLMs, achieving feature parity with commercial models while maintaining complete data sovereignty and offline operation.
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Turning Spoken Commands into JSON Tool Calls on iPhones
A developer demonstrates running local voice-to-JSON inference on iOS devices, enabling on-device speech recognition and structured output generation without cloud dependencies.
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Why Tool Calling is More Important Than Model Size for Local LLMs
A critical perspective on local LLM deployment emphasizes that even the largest models are ineffective without proper tool-calling capabilities. Understanding function calling implementation becomes essential for practical local inference applications.
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Tool Calling Capabilities Essential for Practical Local LLM Agents
XDA analysis reveals that local LLM utility depends critically on tool-calling functionality, not just model size. Tool integration is now table-stakes for production deployments.
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Show HN: Filling PDF Forms with AI Using Client-Side Tool Calling
A new demonstration shows how to use client-side AI tool calling to automate PDF form filling without cloud dependencies. This approach enables privacy-preserving local LLM inference for document processing workflows.
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GLM 5.1 Dominates Agentic Benchmarks, Outperforming Most Models at 1/3 Opus Cost
GLM 5.1 achieves state-of-the-art performance on agentic benchmarks, surpassing most open models and competitive with Claude Opus while remaining viable for local deployment.
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Critical Unsloth Gemma-4 Chat Template Updates for Tool Calling
Unsloth has released updated Gemma-4 quantizations with corrected chat templates and reasoning budget fixes from Google, requiring users to redownload for proper tool calling functionality.
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Gemma 4 Template Improvements Enhance Tool Use and Dialog Compliance
An update to Gemma 4's Jinja templates improves tool calling and dialog compliance, requiring users to update their local model configurations for better results.
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Gemma 4 26B Achieves Impressive Local Performance With Proper Configuration
Users report Gemma 4 26B delivering 80-110 tokens/second on RTX 3090 with excellent tool-calling reliability when properly configured. The model demonstrates significant improvements over previous versions in both speed and functionality for local deployment.
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Book on AI Agents for the Layman: Understanding Agent-Based Systems
A new resource explores AI agents in accessible terms, helping developers understand agent architecture and design patterns relevant to local LLM deployments.
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OmniCoder-9B: Efficient Coding Model for 8GB GPUs
OmniCoder-9B emerges as a high-performance coding and tool-calling model optimized for consumer-grade hardware, delivering sophisticated code generation on limited VRAM budgets.
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Open WebUI Adds Native Terminal Tool Calling with Qwen3.5 35B Support
Open WebUI has integrated native tool calling and open terminal functionality, enabling direct system command execution through Qwen3.5 35B. This breakthrough allows local LLM deployments to interact with system environments in real-time, significantly expanding their practical applications.
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Qwen3.5-35B Unsloth Dynamic GGUFs Achieve SOTA Across Nearly All Quantisation Levels
New state-of-the-art GGUF quantisations for Qwen3.5-35B released with 150+ KL Divergence benchmarks and 9TB of variants. Critical tool calling chat template bug fixed affecting all quantisation uploaders.