Yhyu13/LMCocktail-10.7B-v1-function-calling

TEXT GENERATIONConcurrency Cost:1Model Size:10.7BQuant:FP8Ctx Length:4kLicense:llama2Architecture:Transformer0.0K Open Weights Cold

Yhyu13/LMCocktail-10.7B-v1-function-calling is a merged language model based on LMCocktail-10.7B-v1, specifically fine-tuned for function calling capabilities. This model excels at interpreting and generating structured function calls, achieving 9/10 success on the OpenAI function calling cookbook. It is designed for applications requiring robust tool use and integration with external functions, offering improved grounding compared to previous variants.

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LMCocktail-10.7B-v1-function-calling Overview

This model is a specialized variant of the LMCocktail-10.7B-v1 base model, enhanced with function calling capabilities through a fine-tuning LORA. Developed by Yhyu13, it is designed to enable large language models to interact with external tools and APIs by generating structured function calls.

Key Capabilities

  • Robust Function Calling: Achieves a high success rate (9/10) on the OpenAI function calling cookbook, indicating strong performance in interpreting and executing function call prompts.
  • Improved Grounding: Offers better grounding ability for function calling prompts compared to the developer's previous ph-2 variant.
  • XML-based Structure: Function calls are wrapped in simple XML tags (<functioncall>...</functioncall>) for easy identification and extraction, with a similar structure for function responses (<functionresponse>...</functionresponse>).
  • Integration Ready: Accompanied by a pull request for text-generation-webui to enable GPT-like function calling, suggesting ease of integration into existing LLM applications.

Good For

  • Tool-use Applications: Ideal for scenarios where LLMs need to interact with external tools, databases, or APIs.
  • Agentic Workflows: Suitable for building agents that can perform actions by calling specific functions based on user input.
  • Drop-in Replacement: Can serve as a replacement for applications (e.g., MemGPT) that require LLMs with reliable function calling abilities.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
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frequency_penalty
presence_penalty
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