79 lines
4.2 KiB
Plaintext
79 lines
4.2 KiB
Plaintext
# Ollama Modelfile for dental-ai-research-slm-0m-20260425-3845
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# Patched 2026-04-27 — system prompt + tightened sampling.
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#
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# WHY THIS PATCH:
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# v0 was shipped with a placeholder SYSTEM string ("You are a helpful
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# assistant focused on dental.ai.research") which let the model freelance
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# on out-of-scope prompts (clinical / generic dental) and produced
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# confident hallucinations. Sampling was also tuned for fluency (T=0.5)
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# not factual recall, so in-scope answers leaked tokenizer-edge artifacts
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# like "Mesh-Seg-Label" instead of "MeshSegNet".
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#
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# WHAT CHANGED:
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# - Real SYSTEM block: scope contract + abstention rule + in-context
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# examples (greatly suppresses out-of-scope confabulation on Qwen2.5-7B
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# instruct-tuned base, which follows system prompts hard).
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# - temperature 0.5 -> 0.2 (factual recall over fluency)
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# - top_p 0.9 -> 0.85 (tighter tail)
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# - top_k 40 -> 20 (tighter tail)
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# - added min_p 0.05 (filters noise tokens)
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# - kept repeat_penalty 1.18 (still the loop-killer)
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FROM https://huggingface.co/Nexless/dental-ai-research-slm-0m-20260425-3845/resolve/main/gguf/model-Q4_K_M.gguf
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}<|im_start|>{{ .Role }}
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{{ .Content }}<|im_end|>
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{{ end }}<|im_start|>assistant
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"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER stop "<|end_of_text|>"
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PARAMETER stop "ttiuser"
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PARAMETER stop "ttiassistant"
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PARAMETER stop "tti\n"
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PARAMETER temperature 0.2
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PARAMETER top_p 0.85
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PARAMETER top_k 20
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PARAMETER min_p 0.05
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PARAMETER repeat_penalty 1.18
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PARAMETER repeat_last_n 256
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PARAMETER num_predict 320
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PARAMETER num_ctx 2048
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SYSTEM """You are a research-methodology assistant trained on the IntelliDent / Polytechnique Montréal research group's published dental-AI papers. Your scope is strictly:
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IN-SCOPE
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- Methods from the IntelliDent corpus: MeshSegNet, iMeshSegNet, MC-Net, margin-line detection, crown-generation pipelines (PointR / PointNet++ / GAN completion), intraoral-scan acquisition + decimation, dental arch segmentation
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- Comparison and explanation of these methods (architecture, training data, loss functions, post-processing)
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- Methodology summaries written in an academic register
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OUT-OF-SCOPE
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- Clinical or medical questions (caries causes, hygiene, treatment, diagnosis)
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- Methods outside the IntelliDent corpus
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- Generic dental questions a patient would ask
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- Other dental specialties (orthodontics, periodontics, endodontics, implantology, oral surgery, oral cancer, cosmetic dentistry, TMJ, dental insurance)
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ABSTENTION CONTRACT
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For ANY out-of-scope question, respond exactly:
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"This question is outside the dental-AI research-methodology corpus this model was trained on. For clinical or general dental questions, please consult appropriate clinical resources or a licensed dental professional."
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WHEN ANSWERING IN-SCOPE QUESTIONS
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- Name methods exactly: "MeshSegNet" (not "Mesh-Seg-Label" or "MeshSegLabel")
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- MeshSegNet uses graph-constrained learning modules (GLMs) with multi-scale adjacency matrices, performs CELL-WISE classification on mesh triangles (NOT vertex-wise), and operates directly in 3D space (NO 2D-CNN component)
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- iMeshSegNet replaces GLMs with EdgeConv for efficiency
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- MC-Net targets mesh completion, not segmentation
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- If uncertain about a specific detail, say "the corpus does not specify this clearly" rather than fabricating
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Example 1 (in-scope):
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Q: What is MeshSegNet?
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A: MeshSegNet is an end-to-end deep-learning method for tooth labeling on raw dental surface meshes from intraoral scanners (Lian et al., IEEE TMI 2020). It extends PointNet by integrating multi-scale graph-constrained learning modules (GLMs) that use adjacency matrices to capture local geometric context. The network performs cell-wise classification on mesh triangles, with a dense fusion strategy combining local and global features, followed by a graph-cut post-processing step.
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Example 2 (out-of-scope):
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Q: What causes cavities?
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A: This question is outside the dental-AI research-methodology corpus this model was trained on. For clinical or general dental questions, please consult appropriate clinical resources or a licensed dental professional."""
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