Choosing models for autonomous agents has moved from rumor to real-world playbooks. The big lesson: test many models in parallel, then balance accuracy, speed, and size. [1]
Discovery & Comparison Developers kick off with discovery on the Hugging Face catalog, benchmarks, and community threads, then run side-by-side tests with the same prompts to gauge true trade-offs. [1] Some start large and iterate down via quantization to hit the sweet spot; others stick with a single model and adjust the system prompt. [1] Key metrics: latency, cost, token usage, speed, and accuracy. [1]
Deployment & Usability From the llama.cpp world, the friction is real: no universal server GUI; wrappers around the inference engine exist. [2] llama.cpp is an inference engine; wrappers like Webollama and OpenWebUI exist, but a clean server GUI is missing. [2]
Testing Strategy Test strategy echoes the discovery approach: run prompts across multiple models in parallel, compare results, and track the metrics above. [1] - Latency, cost, token usage, speed, accuracy. [1]
AgentFlow in Practice Case in point: AgentFlow's Flow-GRPO approach reportedly outperforms 200B GPT-4o using a 7B model. [3] Its Google Search tool uses Gemini 2.5 Flash, which can complicate the architecture. [3]
Closing thought: build a flexible framework that prioritizes real testing and clear metrics, and you’ll land an agent that fits your constraints. [3]
References
How do you discover & choose right models for your agents? (genuinely curious)
Explores how to discover, compare, and pick LLMs for agents; factors include accuracy, speed, size; testing strategies discussed and benchmarks
View sourceMore LLM related questions, this time llama.cpp
Debate about llama.cpp usability, GUI wrappers, server UI, and comparison with Ollama, OpenWebUI, and LM Studio on Linux
View sourceStanford Researchers Released AgentFlow: Flow-GRPO algorithm. Outperforming 200B GPT-4o with a 7B model! Explore the code & try the demo
AgentFlow claims 7B beats 200B GPT-4o; discussion of Google/Gemini tooling, backend LLMs, and skepticism about results.
View source