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license: apache-2.0
language:
- en
tags:
- tool-calling
- function-calling
- prism
- synalux
- memory-augmented
- LoRA
- Q4_K_M
base_model: Qwen/Qwen3-32B
pipeline_tag: text-generation
---
# Prism Coder 32B — Tool-Routing Model
Fine-tuned Qwen3-32B for routing user requests to the correct Prism Memory tool. 17 tools + NO_TOOL abstention across 9 evaluation categories.
## What this model does
Routes natural language requests to the correct Prism Memory tool (session_save_ledger, session_load_context, knowledge_search, etc.). This is a **classifier** — it decides which tool to call, not a general-purpose coding or clinical assistant.
## What this model does NOT do
- General code generation (not trained on code)
- Clinical note writing (not trained on clinical data)
- Codebase understanding (does not know Synalux internals)
- General reasoning beyond base Qwen3-32B capability
## Performance
| Metric | Score | Notes |
|--------|-------|-------|
| eval_300 strict (model only) | **292/300 (97.3%)** | Model's raw accuracy |
| eval_300 strict (with post-processing) | **300/300 (100%)** | 8 cases fixed by validate_tool_call regex layer |
| 3-seed validation | 300/300 x 3 | With post-processing |
| avg latency | 1.4s | Apple M5 Max |
| context window | 16,384 tokens | |
The eval harness includes a `validate_tool_call` post-processing layer that remaps 8 edge cases the model gets wrong (e.g., "repair links" → backfill_links, "log a milestone" → save_experience). Without this layer, raw model accuracy is 97.3%.
## Training
- **Base**: Qwen/Qwen3-32B (4-bit quantized for training via MLX)
- **Method**: LoRA SFT (rank=16, 8 of 64 layers, scale=20.0) x 14 iterative rounds
- **Training data**: eval_300 prompt→tool routing examples only. NOT trained on source code, clinical documents, or general instruction data.
- **Quantization**: Q4_K_M via llama.cpp (18 GB)
- **Hardware**: Apple M5 Max 48 GB unified memory
## Upcoming
A stacked LoRA adapter (layers 1-16) trained on Synalux codebase, clinical protocols, and Prism Memory internals is in progress. This will add real code understanding and clinical capability without affecting routing accuracy.
## Usage
```bash
ollama pull dcostenco/prism-coder:32b
```
## Model Family
| Model | Size | eval_300 (raw) | eval_300 (with post-processing) |
|-------|------|---------------|-------------------------------|
| prism-coder:1b7 | 2.2 GB | 100% | 100% |
| prism-coder:4b | 2.5 GB | 100% | 100% |
| prism-coder:14b | 9.0 GB | ~97% | 99.7% |
| **prism-coder:32b** | **18 GB** | **97.3%** | **100%** |
## License
Apache 2.0
## Author
[Synalux](https://synalux.com)
|