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lucent517
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Model Details
Model Provider
lucent517
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
The model is one stage of a pipeline, not a standalone assistant. Upstream, a
CBCT volume and its mask are rendered into a fixed set of images — a panoramic
reconstruction, 3D surface views, maxillary sinus crops, and one close-up
composite per present tooth. The model is then asked ~38 questions per case, each
one sent with the images that can actually settle it and a JSON Schema as
OpenAI-standard response_format. Downstream, deterministic rules reconcile the
answers across image sources and a string template writes the report.
So it expects those renders and those prompts. It is not a general-purpose chat model for dental radiographs, and it will not give useful free-text answers about an arbitrary CBCT slice or photograph.
Official ToothFairy4 ranking on the 40-case validation split, scored end-to-end
(renders → VQA → rule-based postprocess → templated report) against the reference
reports, with a local Qwen3-14B judge for RadFact:
| metric | score |
|---|---|
| Final score | 0.4658 |
| Clinical score (RadFact logical F1) | 0.5139 |
| ├ logical precision | 0.5306 |
| └ logical recall | 0.4982 |
| Captioning (BLEU-4 + METEOR) | 0.2737 |
These are pipeline numbers. The checkpoint's own contribution is the structured extraction; the rendering and the report writer are fixed and deterministic, and changing either moves the score without retraining.
LoRA on a frozen base, trained against bf16 Qwen3.5-9B and merged tensor-wise
into the AWQ checkpoint that is served. The 214 targeted tensors are bit-identical
between the two bases, so the merge is exact rather than an approximation;
provenance for this checkpoint is in merge_meta.json.
| Method | LoRA, rank 16, α 32, dropout 0.05, bias none |
| Placement | 214 modules — 108 in the vision tower (27 blocks × 4), 2 in the vision–language merger, 32 in the full-attention layers, 72 in the GatedDeltaNet layers |
| Trainable | 22,928,896 (0.24 % of the 9.41 B base) |
| Data | 9,520 VQA calls over 528 CBCT cases; 30 cases held out for eval loss |
| Schedule | 2 epochs, LR 1e-4 cosine with 3 % warm-up, effective batch 16, bf16, gradient checkpointing |
| Loss | Token cross-entropy on the values of supervised fields only — not field names, braces or separators |
| Hardware | 1 × A100 40 GB, ~12.5 h |
Targets are built from the mask and the reference reports, so supervision is on the clinical decision each field asks for rather than on report prose.
Runs under vLLM on a single 24 GiB card; weights are 11.2 GiB.
bash
vllm serve lucent517/Qwen3.5-9B-AWQ-dental-cbct-sft \ --max-model-len 32768 \ --limit-mm-per-prompt '{"image": 3}' \ --gpu-memory-utilization 0.90Do not shorten --max-model-len: the context has to hold a ~12.5 k-character
prompt and up to 8,192 tokens of JSON reply. At 8192 the larger calls fail with
a 400 and the pipeline quietly falls back to defaults.
Questions are sent one per request with the schema as response_format; greedy
decoding (temperature=0) is recommended for reproducibility.
Qwen/Qwen3.5-9B, AWQ 4-bit quantized.
The 4-bit kernels need compute capability 8.0+ (Ampere or newer); a T4 will not
run it. Apache-2.0, inherited from the base.
vllm serve lucent517/Qwen3.5-9B-AWQ-dental-cbct-sft \ --max-model-len 32768 \ --limit-mm-per-prompt '{"image": 3}' \ --gpu-memory-utilization 0.90