docs: refresh model card with verified May 14 eval + features (no '100%' hype)

README.md

@@ -2,208 +2,104 @@
 base_model: Qwen/QwQ-32B
 library_name: peft
 pipeline_tag: text-generation
+license: apache-2.0
+language: en
 tags:
-- base_model:adapter:Qwen/QwQ-32B
 - lora
 - sft
-- transformers
-- trl
+- function-calling
+- tool-use
+- mcp
+- aac
+- prism-coder
 ---
 
-# Model Card for Model ID
+# prism-coder:32b (v19)
 
-<!-- Provide a quick summary of what the model is/does. -->
+LoRA fine-tune of **Qwen/QwQ-32B** for offline MCP tool routing — Synalux Copilot's "reasoning tier", used when 14B can't decide or when a complex multi-step prompt arrives.
 
+## Test results — Prism routing 100-case eval (May 14 2026)
 
+3-run mean across seeds 2027/2028/2029 — variance was essentially zero (±0.6%).
 
-## Model Details
+| Category | Score |
+|---|---|
+| **Overall** | **93.7% ± 0.6%** |
+| session_load_context | 100% |
+| session_save_ledger | 100% |
+| session_search_memory | 100% |
+| session_save_handoff | 100% |
+| session_compact_ledger | 100% |
+| brave_web_search | 100% |
+| knowledge_search | 100% |
+| AAC plain-text | 79% |
+| translate plain-text | 83% |
+| static facts (pred) | 100% |
+| live-info refusal | 67% |
+| info / lookup | 100% |
+| edge (multi-step) | 82% |
+| **avg latency** | 2.3 s |
+| **invented tools** | 0 |
 
-### Model Description
+**This is currently the only Prism Coder model that clears the internal 90% routing gate.**
 
-<!-- Provide a longer summary of what this model is. -->
+**What this benchmark measures**: routing precision against the *exact* 7-tool Prism Coder taxonomy. **Not** a general-capability score. Not comparable to public leaderboards. Methodology + runner: [github.com/dcostenco/prism-coder/tree/main/tests/benchmarks/prism-routing-100](https://github.com/dcostenco/prism-coder/tree/main/tests/benchmarks/prism-routing-100).
 
+**Where this model wins**:
+- **All 7 tools routed correctly 100%** of the time (perfect score on every tool category)
+- Zero invented tool names
+- ~2.3 s average latency on a Mac M4 Max — comparable to Claude Sonnet (3.2 s) and Opus (3.0 s) despite running locally
+- Free per-request, private, no rate limits
 
+**Where it underperforms vs Claude Sonnet 4 / Opus 4.7** (99% / 98% on the same eval):
+- `irrel` (live-info refusal) — 67% vs 100%. Sometimes calls `brave_web_search` for "I'm hungry" / "What time is it?" instead of replying in plain text.
+- `aac` — 79% vs 100%. Occasionally tries to route AAC phrase requests to a tool instead of generating phrases directly.
+- `translate` — 83% vs 100%. Same pattern — over-eager tool calls on plain-text intents.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+For production: the [Synalux router](https://github.com/dcostenco/prism-coder) routes complex prompts here and falls through to Claude when this model refuses or invokes the wrong tool.
 
-### Model Sources [optional]
+## Notes on QwQ-32B base
 
-<!-- Provide the basic links for the model. -->
+QwQ-32B is a reasoning-tuned variant that natively emits `<think>...</think>` blocks. For routing, that's overhead — the Ollama Modelfile uses a `nothink` template (empty `<think></think>` block in the assistant prefix) to skip reasoning and go straight to the tool call. Without `nothink`: 97% (single-seed); with `nothink` + surgical prompt disambiguation: 93.7% stable. See [`Modelfile.32b`](https://github.com/dcostenco/prism-coder/blob/main/tests/benchmarks/prism-routing-100/Modelfile.32b).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Training recipe (v19)
 
-## Uses
+- **Base**: Qwen/QwQ-32B
+- **LoRA**: r=32, α=64, dropout 0.05, all 7 target modules (q/k/v/o + gate/up/down)
+- **Corpus**: ~14K-row composite (Phase 1 general + Phase 2 agentic + Phase 3 multi-turn XL)
+- **Hardware**: RunPod A100 80GB / RTX 6000 Ada
+- **Quantization**: published as Q4_K_M GGUF (~19 GB) and the merged HF safetensors
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Usage
 
-### Direct Use
+### Ollama (recommended)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```bash
+ollama pull dcostenco/prism-coder:32b
+ollama run dcostenco/prism-coder:32b "Save handoff for prism-coder — deployment complete"
+```
 
-[More Information Needed]
+### HuggingFace (transformers + PEFT)
 
-### Downstream Use [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base = AutoModelForCausalLM.from_pretrained("Qwen/QwQ-32B", torch_dtype="auto")
+model = PeftModel.from_pretrained(base, "dcostenco/prism-coder-32b")
+tok = AutoTokenizer.from_pretrained("Qwen/QwQ-32B")
+```
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### System prompt
 
-[More Information Needed]
+Use the [v25 routing prompt](https://github.com/dcostenco/prism-coder/blob/main/tests/benchmarks/prism-routing-100/benchmark.py#L47) verbatim, with the `nothink` template (see `Modelfile.32b`).
 
-### Out-of-Scope Use
+## Hardware requirements
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Mac**: M2 Ultra+ with ≥48 GB unified memory (Q4_K_M = 19 GB + activations)
+- **Linux + NVIDIA**: A100 40GB+, H100, B200, or 2× RTX 4090
+- **Inference speed**: ~2–5 s per 200-token response (varies by hardware)
+- **Loaded VRAM**: ~22 GB
 
-[More Information Needed]
+## License
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.19.1
+Inherits QwQ-32B's Apache-2.0 license. The LoRA delta is also Apache-2.0.