The llama2 model is giving me the function that needs to be executed together with the appropriate arguments based on the user prompt. In order for the model to remember the chat's context, I want to pass the function's response back to it. How these things can be accomplished.
Have you noticed any significant latency when handling function calls with this model version? I've encountered some inconsistencies in response times compared to vanilla Llama-2, especially when invoking external tools. Any insights or tweaks that could stabilize performance would be appreciated.
What’s the tool-call success rate when using 4-bit quantization in a 7B model with a 128-token context? I’ve seen inconsistencies in tool return parsing when prompt templates include dynamic variables—does the model retain state across tool invocations or reset after each function call? Also, any observed latency spikes during function execution in the last 10 tokens? Real-world stability matters more than idealized benchmarks.
Tool-call stability drops below 80% at >1000 tokens in prompt—does this happen due to prompt length or function call overhead? Also, does the v2 function-calling layer introduce token leakage when tools are invoked mid-prompt? Observed 3–5% increase in token cost per function call in 7B baseline. Any quantization artifacts when using Q4_K_M in 4-bit? Specific to tool selection or execution timing?
Tool-call stability drops after 3+ consecutive function invocations in 7B models—observed with prompt templates that include dynamic variable injection. Inference time spikes by ~180ms per call when using JSON-formatted tool outputs in non-structured prompts. Quantization to Q4_K_M causes tool response truncation in 12% of cases with long output chains. Local runs show tool-call failure rates increase when model context window exceeds 32k tokens. These behaviors are inconsistent across prompt templates—specifically, structured JSON headers reduce failure rates by 40%.
