CodeXomics-ToolAgent-4B-v1 (internally qwen3.5:4b-codexomics-tools-v5) is a 4.2B-parameter
tool-calling model fine-tuned from Qwen/Qwen3.5-4B for the CodeXomics genomics workbench. It
performs native function calling against the CodeXomics tool registry (file loading, navigation,
sequence analysis, annotation, track control, export, BLAST, primer design, database/protein
retrieval, task management, and UI control).
CodeXomics is an AI-native genome browser: a cross-platform desktop application in which
conversational AI agents drive genome visualization and run real biological analyses, with built-in
tool execution, a dynamic tool registry, plugin development, and Model Context Protocol (MCP)
integration. Source code and documentation:
github.com/Scilence2022/CodeXomics and
scilence2022.github.io/CodeXomics.
Maximum sequence length: 3,072 tokens; prompt masking enabled
Hardware: Apple M3 Max; peak memory 191 GB (including swap)
Checkpoint selection: iteration 75 (validation loss 0.020); test loss 0.074 (perplexity 1.077)
Training data: CodeXomics-ToolCalling-v1 (373/123/30 train/validation/test examples)
Evaluation
On the CodeXomics Benchmark (172 automatic tests: 143 single-operation, 29 multi-step), evaluated
in the real application loop with task-completion scoring plus execution evidence. Both the
fine-tuned model and the un-fine-tuned Qwen3.5-4B baseline were evaluated in three independent
complete sessions; results were identical across sessions for both models.
Suite
Qwen3.5-4B (base)
CodeXomics-ToolAgent-4B-v1
Simple
139/143
143/143
Complex
26/29
29/29
Total
165/172 (95.9%)
172/172 (100%)
Fine-tuning improved the overall accuracy by 7 tests (+4 simple, +3 complex). Inference settings:
temperature 0, thinking enabled.
Inference speed (mean ± SD over three independent runs; offline harness, 172 tests, Ollama Q4_K_M
on Apple M3 Max):
Metric
Qwen3.5-4B (base)
CodeXomics-ToolAgent-4B-v1
Average latency per test (s)
12.0 ± 0.1
10.7 ± 0.9
Simple suite (s)
9.2 ± 0.1
8.6 ± 0.7
Complex suite (s)
25.8 ± 0.3
21.5 ± 1.9
Average latency per tool call (s)
7.9 ± 0.1
7.3 ± 0.6
Generation throughput (tokens/s)
33.9 ± 0.4
33.9 ± 2.8
Generated tokens per test
408 (identical across runs)
363 (identical across runs)
Prompt tokens per test
9,004 (identical across runs)
8,777 (identical across runs)
CodeXomics Benchmark: base vs. fine-tuned 4B
Figure 1. Two-panel comparison between the un-fine-tuned Qwen3.5-4B baseline and
CodeXomics-ToolAgent-4B-v1. Left: CodeXomics Benchmark pass rates (simple/complex/total; three
sessions each, identical results). Right: average inference latency per test with error bars
(mean ± SD over three runs; Ollama Q4_K_M, Apple M3 Max). Latency values are the mean of the
per-suite rows above; error bars show the run-to-run SD.
For deployment in CodeXomics, the model is served through Ollama as qwen3.5:4b-codexomics-tools-v5
(Q4_K_M, 2.7 GB) with native tool calling and thinking enabled.
Limitations
The model is specialized for CodeXomics genomic workflows; generalization to other tool-calling
domains was not evaluated.
