初始化项目，由ModelHub XC社区提供模型

Model: Nexless/dental-ai-research-slm-0m-20260425-3845 Source: Original Platform
2026-05-15 11:38:17 +08:00
commit 1f336d4e99
15 changed files with 152217 additions and 0 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1,38 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+gguf/model-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/model-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
--- a/1
+++ b/1
@@ -0,0 +1 @@
+This model is released under the apache-2.0 license.
--- a/78
+++ b/78
@@ -0,0 +1,78 @@
+# Ollama Modelfile for dental-ai-research-slm-0m-20260425-3845
+# Patched 2026-04-27 — system prompt + tightened sampling.
+#
+# WHY THIS PATCH:
+#   v0 was shipped with a placeholder SYSTEM string ("You are a helpful
+#   assistant focused on dental.ai.research") which let the model freelance
+#   on out-of-scope prompts (clinical / generic dental) and produced
+#   confident hallucinations. Sampling was also tuned for fluency (T=0.5)
+#   not factual recall, so in-scope answers leaked tokenizer-edge artifacts
+#   like "Mesh-Seg-Label" instead of "MeshSegNet".
+#
+# WHAT CHANGED:
+#   - Real SYSTEM block: scope contract + abstention rule + in-context
+#     examples (greatly suppresses out-of-scope confabulation on Qwen2.5-7B
+#     instruct-tuned base, which follows system prompts hard).
+#   - temperature 0.5 -> 0.2  (factual recall over fluency)
+#   - top_p       0.9 -> 0.85 (tighter tail)
+#   - top_k       40  -> 20   (tighter tail)
+#   - added       min_p 0.05  (filters noise tokens)
+#   - kept        repeat_penalty 1.18 (still the loop-killer)
+
+FROM https://huggingface.co/Nexless/dental-ai-research-slm-0m-20260425-3845/resolve/main/gguf/model-Q4_K_M.gguf
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}<|im_start|>assistant
+"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "<|im_start|>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER stop "<|end_of_text|>"
+PARAMETER stop "ttiuser"
+PARAMETER stop "ttiassistant"
+PARAMETER stop "tti\n"
+
+PARAMETER temperature 0.2
+PARAMETER top_p 0.85
+PARAMETER top_k 20
+PARAMETER min_p 0.05
+PARAMETER repeat_penalty 1.18
+PARAMETER repeat_last_n 256
+PARAMETER num_predict 320
+PARAMETER num_ctx 2048
+
+SYSTEM """You are a research-methodology assistant trained on the IntelliDent / Polytechnique Montréal research group's published dental-AI papers. Your scope is strictly:
+
+IN-SCOPE
+- 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
+- Comparison and explanation of these methods (architecture, training data, loss functions, post-processing)
+- Methodology summaries written in an academic register
+
+OUT-OF-SCOPE
+- Clinical or medical questions (caries causes, hygiene, treatment, diagnosis)
+- Methods outside the IntelliDent corpus
+- Generic dental questions a patient would ask
+- Other dental specialties (orthodontics, periodontics, endodontics, implantology, oral surgery, oral cancer, cosmetic dentistry, TMJ, dental insurance)
+
+ABSTENTION CONTRACT
+For ANY out-of-scope question, respond exactly:
+"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."
+
+WHEN ANSWERING IN-SCOPE QUESTIONS
+- Name methods exactly: "MeshSegNet" (not "Mesh-Seg-Label" or "MeshSegLabel")
+- 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)
+- iMeshSegNet replaces GLMs with EdgeConv for efficiency
+- MC-Net targets mesh completion, not segmentation
+- If uncertain about a specific detail, say "the corpus does not specify this clearly" rather than fabricating
+
+Example 1 (in-scope):
+Q: What is MeshSegNet?
+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.
+
+Example 2 (out-of-scope):
+Q: What causes cavities?
+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."""
--- a/README.md
+++ b/README.md
@@ -0,0 +1,338 @@
+---
+license: apache-2.0
+language: en
+library_name: peft
+pipeline_tag: text-generation
+tags:
+  - text-generation
+  - qlora
+  - lora
+  - peft
+  - dental
+  - medical
+  - domain-specialized
+  - 7b
+  - gguf
+base_model: Qwen/Qwen2.5-7B-Instruct
+---
+
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/c93f0dcd-456f-48a0-bc96-b589ead27c19" alt="SLM-Forge banner" width="100%">
+</p>
+
+<p align="center">
+  <a href="https://github.com/Dshamir/slm-forge"><img src="https://img.shields.io/badge/SLM--Forge-source%20on%20GitHub-181717?logo=github" alt="GitHub: Dshamir/slm-forge"></a>
+  <a href="https://github.com/Dshamir/slm-forge"><img src="https://img.shields.io/github/license/Dshamir/slm-forge?color=blue" alt="License: MIT"></a>
+  <a href="https://docs.claude.com/en/docs/claude-code"><img src="https://img.shields.io/badge/Built%20with-Claude%20Code-D97757?logo=anthropic&logoColor=white" alt="Built with Claude Code"></a>
+  <img src="https://img.shields.io/badge/status-PoC-orange" alt="Status: PoC">
+</p>
+
+> 🔧 **Toolkit:** This model was forged with [**SLM-Forge**](https://github.com/Dshamir/slm-forge) — a public, MIT-licensed, semi-autonomous skill tree for the Claude Code TUI that takes you from corpus + budget to a trained + evaluated + quantized + published Small Specialty Language Model in a single session, with one human gate. The dental run is the worked case study; the toolkit is yours to fork and use on your own corpora.
+
+# dental-ai-research-slm — Qwen2.5-7B-Instruct + IntelliDent dental-AI research LoRA (v0)
+
+A **research-methodology assistant**, not a clinical assistant. LoRA fine-tune of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on a curated corpus of dental-AI research papers from the IntelliDent / Polytechnique Montréal group — covering tooth segmentation (MeshSegNet, iMeshSegNet, MC-Net), preparation margin-line detection, crown generation (PointR / PointNet++), intraoral 3D scan processing, and adjacent dental imaging research.
+
+> ## ⚠️ The slug says `slm-0m`. The model is **not** a 0M-parameter model.
+>
+> `slm-0m` is a **forge-tool version label** ("v0 milestone") generated by the templating pipeline that produced this artifact. It does **not** describe the model size.
+>
+> **Actual size:**
+> - Base: **Qwen/Qwen2.5-7B-Instruct** — 7.62 B parameters
+> - Trainable LoRA adapter: **~20 M parameters** (0.46 % of base)
+>
+> Future milestones will use clearer slugs (`v0`, `v0.1`, `v1`).
+
+## ✅ What this model is for / 🚫 what it is not for
+
+| ✅ In-scope | 🚫 Out-of-scope (model abstains) |
+|---|---|
+| MeshSegNet / iMeshSegNet / MC-Net architectures | Clinical questions (caries, hygiene, treatment, diagnosis) |
+| Margin-line detection methods | Generic dental Qs a patient would ask |
+| Crown-generation pipelines (PointR, PointNet++, GAN completion) | Other specialties: orthodontics, periodontics, implantology, oral surgery, oral cancer, TMJ, cosmetic dentistry |
+| Intraoral 3D scan acquisition + decimation | Methods outside the IntelliDent corpus |
+| Method comparison / methodology summaries in an academic register | Drug design, dental insurance, clinical recommendations |
+
+**Abstention contract.** When asked anything out-of-scope, the model is instructed to answer exactly:
+
+> "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."
+
+The abstention contract is enforced via the system prompt baked into the shipped [`Modelfile`](./Modelfile). **For Transformers / PEFT users:** include the same system prompt (excerpted below in *How to use*) — without it the model will freelance on out-of-scope prompts.
+
+## What's in this repo
+
+| File / dir | Purpose | Size |
+|---|---|---|
+| `adapter_model.safetensors` + `adapter_config.json` | LoRA adapter (r=32, α=64) — apply on top of `Qwen/Qwen2.5-7B-Instruct` via PEFT | ~80 MB |
+| `gguf/model-Q4_K_M.gguf` | Merged + quantized 4-bit GGUF — best size/quality trade-off; runs on a modern laptop | ~4.7 GB |
+| `gguf/model-Q8_0.gguf` | Merged + quantized 8-bit GGUF — near-lossless | ~8.1 GB |
+| `Modelfile` | Ollama Modelfile pre-configured with system prompt + sampling defaults (see below) | — |
+| `tokenizer.json` / `vocab.json` / `merges.txt` / `chat_template.jinja` | Qwen2 tokenizer + ChatML chat template | — |
+| `LICENSE` | Apache-2.0 (inherited from base) | — |
+
+## Training
+
+| | |
+|---|---|
+| **Base model** | `Qwen/Qwen2.5-7B-Instruct` (Apache-2.0) |
+| **Regime** | QLoRA — base loaded in 4-bit NF4 (bitsandbytes), LoRA adapter trained in bf16 |
+| **LoRA config** | rank 32, alpha 64, dropout 0.05; targets `q_proj, k_proj, v_proj, o_proj` |
+| **Trainable params** | 20,185,088 (0.46 % of 4.37 B post-quantization) |
+| **Sequence length** | 2048 tokens |
+| **Effective batch** | 8 (micro-batch 2 × grad-accum 4) |
+| **Steps** | 900 (~2.94 epochs over 2,209 train examples) |
+| **Learning rate** | 1e-4, cosine schedule, gradient checkpointing on |
+| **Hardware** | AWS `g5.2xlarge` — 1× NVIDIA A10G (24 GB), 8 vCPU, 32 GB RAM, ca-central-1 |
+| **Train wall-clock** | 5 h 53 min |
+| **Total forge wall-clock** | 9 h 03 min (training + provisioning + repeated phase reruns from in-flight bug fixes) |
+| **AWS spend** | $11.62 (on-demand g5.2xlarge × 8 h actual EC2 + Claude API for synth + plan-fit grading) |
+
+## Training data
+
+A 320-document corpus of dental-AI research artifacts (PDFs of theses, journal papers, conference proceedings: PolyMtl, MICCAI, SPIE, ISBI, JBHI, JMI, IEEE-EMBC, MEDIA, JDentistry, Computer Biology & Medicine, plus internal IVADO presentations).
+
+> **Scope of v0's corpus (important for setting expectations).**
+> v0 was forged from the original `corpora/publications-raw/Publications` directory, which physically contained only **4 file types** — PDF / DOCX / PPTX / TXT. v0 therefore did **not** see the rich-media extensions present in the broader IntelliDent archive (XLSX/CSV spreadsheets, PNG/JPG/TIF/HEIC figures, MP4/m4a/wav screen-recordings, STL/VTP/OBJ/PLY meshes, MyISAM EndNote bibliographies, ZIP archives). Those are queued for **v1** — see [Roadmap](#roadmap).
+
+| Stage | Output |
+|---|---|
+| `prep` | 2,448 raw passages extracted from PDF/DOCX/PPTX/TXT (`pdf=2041, docx=163, pptx=241, txt=3`) |
+| `audit` | 843 passages retained (34.4 %) after MinHash dedup, length filter, language filter, domain density check, contamination scrub. **655,562 clean tokens.** |
+| `synth` | 2,514 Q/A pairs synthesized via Claude Haiku 4.5 (factual / mechanism / clinical Q types) |
+| `synth-filter` | 2,455 Q/A pairs after schema + quality filter |
+| `shape` | 90/5/5 split → 2,209 train / 122 val / 124 test (deterministic shuffle seeded on forge id) |
+
+The corpus is heavily concentrated on:
+- 3D mesh segmentation of dental arches (MeshSegNet, iMeshSegNet, PointNet++)
+- Preparation margin-line detection (regression + classification approaches)
+- AI-driven crown generation (PointR shell prediction, GAN-based completion)
+- Dental scan acquisition + decimation pipelines
+
+## Plan-fit gate (pre-spend validation)
+
+Before any GPU spend, the forge ran a 7-axis Q/A grading gate via Claude Sonnet 4.6:
+
+| Axis | Result |
+|---|---|
+| In-domain classification (Haiku) | **100 %** in domain (threshold: 95 %) |
+| Subdomain coverage | passed |
+| Q/A factual + appropriate + grounded + concise score | mean **4.135 / 5** (threshold: 4.0); min individual **3.0+** |
+| Q/A type diversity | max single type 40 % (threshold: 50 %) |
+| Hyperparameter sanity | passed (rank, epochs, LR, seq-len in safe envelopes) |
+| Training format roundtrip | passed (ChatML round-trips through tokenizer) |
+| Budget headroom | $189.90 remaining of $200 cap (-94.4 % synth overrun → much cheaper than estimated) |
+
+## Evaluation
+
+Post-training perplexity / sample-generation eval **was skipped on this run** due to a bug in the forge's eval phase (loaded base + adapter in fp32 → OOM on the 32 GB instance). The bug is now patched in the upstream forge code; future runs will populate this section.
+
+**Empirical model behavior should be assessed via the GGUFs and the sampling settings below.** Hand-evaluation showed two failure modes that the v0 patch (this artifact, 2026-04-27) targets directly:
+
+1. **Out-of-scope confabulation** — questions like "What causes cavities?" produced confident, plausible-sounding clinical answers instead of declining. **Root cause:** v0 was shipped with a placeholder system prompt. **Fix in this revision:** real system-prompt scope contract + abstention rule baked into the Modelfile.
+2. **In-scope name corruption** — answers about MeshSegNet sometimes leaked tokenizer-edge artifacts ("Mesh-Seg-Label") and sometimes invented architectural components (a 2D-CNN stage). **Root cause:** sampling was tuned for fluency (T=0.5), and the corpus didn't include explicit method-discrimination or negative-fact pairs. **Partial fix in this revision:** sampling tightened (T=0.2, top_p=0.85, top_k=20, min_p=0.05) which kills the name-corruption class. The architectural-confabulation class needs a continuation training run (see *Roadmap*).
+
+## Recommended sampling settings
+
+For factual recall on this narrow corpus (all values pre-baked in the [`Modelfile`](./Modelfile)):
+
+| param | value | reason |
+|---|---|---|
+| `temperature` | **0.2** | factual recall, kills tokenizer-edge corruption like `Mesh-Seg-Label` |
+| `top_p` | **0.85** | tighter tail than v0's 0.9 |
+| `top_k` | **20** | tighter tail than v0's 40 |
+| `min_p` | **0.05** | filters noise tokens |
+| `repeat_penalty` | 1.18 | kills paragraph loops |
+| `repeat_last_n` | 256 | window for the penalty |
+| `max_tokens` | 320 | keep responses short |
+
+**Stop strings** (LM Studio "Stop strings" field, or llama-cli `--reverse-prompt`):
+
+```
+<|im_end|>
+<|im_start|>
+<|endoftext|>
+ttiuser
+ttiassistant
+```
+
+## How to use
+
+> **Always pass a scope-enforcing system prompt.** The Modelfile and Space app bake one in. If you're calling the model via Transformers / PEFT directly, paste the [system prompt block](#system-prompt-for-transformerspeft-users) below into your `apply_chat_template` call.
+
+### Load the LoRA adapter on top of Qwen2.5-7B-Instruct (Transformers + PEFT)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+REPO = "Nexless/dental-ai-research-slm-0m-20260425-3845"
+BASE = "Qwen/Qwen2.5-7B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(REPO)
+base = AutoModelForCausalLM.from_pretrained(
+    BASE,
+    torch_dtype=torch.bfloat16,   # required — fp32 won't fit on a 24 GB GPU
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base, REPO)
+model.eval()
+
+SYSTEM_PROMPT = """You are a research-methodology assistant trained on the IntelliDent / Polytechnique Montréal research group's published dental-AI papers. ...
+(see full text under 'System prompt for Transformers/PEFT users' below)"""
+
+prompt = "How does iMeshSegNet differ from MeshSegNet for dental arch segmentation?"
+inputs = tokenizer.apply_chat_template(
+    [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user",   "content": prompt},
+    ],
+    return_tensors="pt", add_generation_prompt=True,
+).to(model.device)
+
+with torch.no_grad():
+    out = model.generate(
+        inputs,
+        max_new_tokens=320,
+        temperature=0.2,
+        top_p=0.85,
+        top_k=20,
+        repetition_penalty=1.18,
+        do_sample=True,
+    )
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### Use the GGUF in Ollama
+
+```bash
+curl -L -O https://huggingface.co/Nexless/dental-ai-research-slm-0m-20260425-3845/resolve/main/Modelfile
+ollama create dental-research -f Modelfile
+ollama run dental-research
+```
+
+The shipped [`Modelfile`](./Modelfile) already encodes the system prompt + sampling defaults + stop strings.
+
+### Use the GGUF in LM Studio / llama.cpp / text-generation-webui
+
+Download `gguf/model-Q4_K_M.gguf` (4.7 GB, recommended for laptop CPUs) or `gguf/model-Q8_0.gguf` (8.1 GB, higher quality on machines with 16+ GB RAM). In the UI's advanced configuration panel:
+
+1. Paste the system prompt from [System prompt for Transformers/PEFT users](#system-prompt-for-transformerspeft-users) below.
+2. Set the sampling values from the [recommended sampling settings table](#recommended-sampling-settings).
+3. Add the stop strings.
+
+### System prompt for Transformers/PEFT users
+
+```text
+You are a research-methodology assistant trained on the IntelliDent / Polytechnique Montréal research group's published dental-AI papers. Your scope is strictly:
+
+IN-SCOPE
+- 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
+- Comparison and explanation of these methods (architecture, training data, loss functions, post-processing)
+- Methodology summaries written in an academic register
+
+OUT-OF-SCOPE
+- Clinical or medical questions (caries causes, hygiene, treatment, diagnosis)
+- Methods outside the IntelliDent corpus
+- Generic dental questions a patient would ask
+- Other dental specialties (orthodontics, periodontics, endodontics, implantology, oral surgery, oral cancer, cosmetic dentistry, TMJ, dental insurance)
+
+ABSTENTION CONTRACT
+For ANY out-of-scope question, respond exactly:
+"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."
+
+WHEN ANSWERING IN-SCOPE QUESTIONS
+- Name methods exactly: "MeshSegNet" (not "Mesh-Seg-Label" or "MeshSegLabel")
+- 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)
+- iMeshSegNet replaces GLMs with EdgeConv for efficiency
+- MC-Net targets mesh completion, not segmentation
+- If uncertain about a specific detail, say "the corpus does not specify this clearly" rather than fabricating
+
+Example 1 (in-scope):
+Q: What is MeshSegNet?
+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.
+
+Example 2 (out-of-scope):
+Q: What causes cavities?
+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.
+```
+
+## Limitations & caveats
+
+- **Narrow domain.** Out-of-domain generic prompts will degrade vs. base. Best behavior is on dental-AI methodology, mesh / point-cloud segmentation, margin-line / crown-generation pipelines, and adjacent imaging topics. The system prompt + abstention contract suppress most freelancing; if you bypass them, results are not representative.
+- **Small training set.** ~655K clean tokens / 2,455 Q/A pairs is on the lower end for 7B SFT. A larger corpus would improve recall of paper-specific facts.
+- **No baseline perplexity comparison** in this release — see the eval section.
+- **Method-discrimination + negative-fact gaps.** The corpus didn't include explicit "MeshSegNet vs iMeshSegNet" contrastive pairs or "MeshSegNet does NOT use a 2D-CNN" negation pairs, so the model can confabulate plausible-sounding architectural details. The Modelfile's in-context examples patch this in part; a continuation run is the structural fix (see *Roadmap*).
+- **Live demo Space currently broken on free tier.** The auto-published [Gradio Space](https://huggingface.co/spaces/Nexless/dental-ai-research-slm-0m-20260425-3845-demo) tries to load the full 7B in HuggingFace Spaces' free CPU tier (16 GB RAM) and OOMs. Use the GGUF locally instead, or fork the Space onto a paid GPU runtime.
+- **Repo / Space slug `slm-0m`** is a forge-tool version label, **not** a parameter count. Future revisions will use clearer slugs.
+
+## Roadmap
+
+This is **v0**. Two follow-up artifacts are planned:
+
+### v0.1 — continuation training (~3–4 h on A10G, ~$5)
+
+Add three synth buckets to the existing Q/A set and continue-train the LoRA at LR=5e-5 for one epoch:
+
+| Bucket | Pairs | Purpose |
+|---|---|---|
+| **Abstention pairs** | ~250 | Out-of-scope questions paired with the abstention response (3-4 phrasing variants), covering generic hygiene, orthodontics, periodontics, drug design, mesh decimation, dental insurance, cosmetic dentistry, oral cancer, TMJ, implants. Adds learned abstention behavior on top of the system-prompt enforcement. |
+| **Method discrimination pairs** | ~150 | "What's the difference between [method A] and [method B]?" with explicit contrastive answers naming the actual differentiator. Pairs to cover: MeshSegNet vs iMeshSegNet (GLM vs EdgeConv), vs PointNet (mesh vs raw point cloud), vs TSGCNet (single vs dual stream), vs DGCNN; MC-Net vs MeshSegNet (completion vs segmentation). |
+| **Negative-fact pairs** | ~100 | Explicit corrections: "Does MeshSegNet use 2D CNNs? No — MeshSegNet operates directly in 3D space on mesh cells." Negation training is the underused trick — LLMs hallucinate confidently because they've never seen explicit corrections. |
+
+### v1 — full re-forge (expanded corpus, all file types)
+
+v1 moves from `corpora/publications-raw/Publications` (4 file types, 320 docs) to **`corpora2/extracted/Publications`** — **1,547 files spanning 18 extensions** routed through all 19 SLM-Forge prep plugins. New file-type coverage relative to v0:
+
+| Extension family | Plugin | What v0 missed |
+|---|---|---|
+| **PDF / DOCX / PPTX / TXT** | `pdf`, `docx_plugin`, `pptx_plugin`, `text_simple` | (already covered in v0) |
+| **XLSX / CSV** | `tabular` | data tables, references lists in spreadsheets |
+| **EPUB** | `epub` | textbook chapters |
+| **Notebooks (.ipynb)** | `notebook` | analysis code + markdown narratives |
+| **Code (.py / .js / .ts / .cpp / etc.)** | `code` | training scripts, model definitions |
+| **PNG / JPG / TIF / HEIC** | `ocr` (disabled in v1: `FORGE_DISABLE_OCR=1`) | (figures yielded low-value OCR text — explicitly skipped to save tokens + Claude budget) |
+| **MP4 / MKV / MOV / WEBM** | `video` (whitelisted: ≥120 s + audio stream required) | narrated method walkthroughs (~10 of 171 clips have value; rest are silent MeshLab screen-recordings) |
+| **m4a / wav / mp3** | `audio` (faster-whisper local CPU transcribe) | recorded talks, voice memos |
+| **STL / PLY / OBJ / VTP** | `mesh_metadata` | 3D mesh metadata (vertex/face counts, bounding boxes) |
+| **DICOM** | `dicom` | medical imaging headers |
+| **MyISAM (.frm/.myi/.myd/.ibd)** | `mysql_revive` | EndNote bibliographic backups (paper abstracts → high signal) |
+| **EML / MBOX** | `email_plugin` | research correspondence |
+| **GeoTIFF / SHP** | `geo` | (not present here, future-proofing) |
+| **HDF5 / NetCDF** | `scientific` | dataset archives |
+| **ZIP / TAR / RAR** | `archive` | recursive extraction (RAR support via `unrar` system binary) |
+| **Binary (default)** | `binary_metadata` | catch-all metadata extraction |
+
+Plus structural changes targeting the failure modes documented in *Evaluation*:
+- **Smaller base, lower rank.** 7B + r=32 + 1.6 M tokens is over-parameterized for this corpus size; the train/eval ratio is 1.61× which is the overfitting signature. v1 will likely move to a 3B base + r=16 LoRA, with `max_steps=8000` (proper SFT depth, ~1.8 epochs) and a 100-step calibration burst at train start (auto-aborts if sec/step > 27).
+- **New synth buckets baked in.** The v0.1 buckets (abstention + method-discrimination + negative-fact) are integrated from synth phase forward, not bolted on.
+- **MP4 whitelist** + **OCR off** prevent waste on silent screen-recordings + low-text-density figures.
+
+## Reproducibility
+
+Forge run id: `v2-20260424-131000-3845`. Full pipeline definition: `prep → audit → synth → shape → plan_fit → provision → bootstrap → train → monitor → eval → quantize → register → card_validator → smoketest → publish → teardown → report`.
+
+## Citation
+
+```bibtex
+@misc{dental_ai_research_slm_2026,
+  title  = {dental-ai-research-slm: a Qwen2.5-7B-Instruct LoRA fine-tuned on dental-AI research papers},
+  author = {Nexless},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/Nexless/dental-ai-research-slm-0m-20260425-3845}}
+}
+```
+
+---
+
+## ⚠️ Disclaimer — Proof of Concept
+
+This artifact is a **proof of concept** of **semi-autonomous skills running inside the Claude Code TUI**, developed by **Nexless**. The dental-AI research content is published **for educational purposes only** — it is part of a broader experiment testing the capabilities of **SLM — (Small) Speciality Language Models** as a category: how narrow domain corpora, plan-fit pre-spend gates, abstention contracts, and skill-tree-driven training pipelines compose into a publishable, scope-honest small model.
+
+**Not for clinical use.** Not a substitute for licensed dental or medical advice. The model is intentionally narrow (research-methodology paraphrase) and is instructed to abstain on clinical and out-of-scope questions; if it answers anything that looks like clinical advice, that is a failure mode, not a recommendation.
+
+---
+
+*Trained with [SLM-Forge](https://github.com/Dshamir/sif-knowledge-base) — a skill-tree pipeline for scoping, training, evaluating, quantizing, and publishing small-to-mid-size domain language models.*
+
+*Card revised 2026-04-27 to add explicit scope + abstention contract, sampling tightened from v0 defaults, v0.1/v1 roadmap, and PoC disclaimer.*
--- a/adapter_config.json
+++ b/adapter_config.json
@@ -0,0 +1,45 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "q_proj",
+    "k_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}
--- a/adapter_model.safetensors
+++ b/adapter_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5b436425de2dac8688e92a90c7a132174ee54f33fb64fa22b41a81eb29e8ba0
+size 80770432
--- a/added_tokens.json
+++ b/added_tokens.json
@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/gguf/model-Q4_K_M.gguf
+++ b/gguf/model-Q4_K_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7f7bc4bc3a5599bde7012f7888e4a302a5797aa5792cc0faf12fb4deb84f95a
+size 4683073440
--- a/gguf/model-Q8_0.gguf
+++ b/gguf/model-Q8_0.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52f5039eb371d94b02adad1a99e804e1b85fc9bcf5ea2de4e2261a26e3586263
+size 8098525088
--- a/merges.txt
+++ b/merges.txt
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:694f1174c5bdf94e2fc50796c0f1733a5a3945ff110b0dfa40ea0701cc9c9c42
+size 11422176
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
--- a/vocab.json
+++ b/vocab.json
				`@@ -0,0 +1 @@`
				`This model is released under the apache-2.0 license.`