← Case studies
Model · Fine-tune
mdAgent-Hermes-32B
A multimodal tool-use LoRA fine-tune of Qwen3-VL-32B, optimized for reliable function calling and document understanding — quantized to run on a single on-prem GPU node.
- Base model
- Qwen3-VL-32B
- Method
- LoRA · multimodal
- Quantization
- AWQ · INT4
- Internal eval
- 82.3% BFCL-v3 single-turn
Overview
Off-the-shelf models are rarely reliable enough at tool use for production agents — they hallucinate arguments, miss schemas and break on multimodal input. mdAgent-Hermes-32B is a targeted fine-tune that makes a 32B vision-language model dependable at calling the right tool with the right arguments.
Trained with LoRA on curated multimodal tool-use data and then quantized for efficient serving, the model keeps strong function-calling accuracy while fitting a realistic on-prem hardware budget.
On LMXAI’s internal BFCL-v3 single-turn function-calling eval (evalscope), mdAgent-Hermes-32B reached 82.3% success. That is our own run of the Berkeley Function-Calling Leaderboard v3 single-turn suite — not a published third-party leaderboard rank.
Approach
- LoRA fine-tuning of Qwen3-VL-32B on multimodal tool-use traces.
- Quantization (AWQ, INT4) for low-memory, high-throughput inference.
- Rigorous evaluation with evalscope across function-calling and reasoning suites.
- vLLM serving for production deployment on an A100 cluster.
Evaluation
82.3%Internal BFCL-v3 single-turn
evalscopeBFCL-v3 · HumanEval · GSM8K
HumanEvalCode reasoning
GSM8KMath reasoning
Stack
PyTorchLoRAQwen3-VL-32BAWQINT4vLLMevalscopeA100