Delete endoedge_qwen3vl8b_lora_vis_r32

endoedge_qwen3vl8b_lora_vis_r32/step-1250/README.md

@@ -1,93 +0,0 @@
-# Endo_edge — Qwen3-VL-8B-Instruct, LoRA + trained vision encoder (step 1250)
-
-Instruction fine-tune of `Qwen/Qwen3-VL-8B-Instruct` that reads a GI endoscopy frame
-and emits a unified JSON report. Trained on GastroVision + HyperKvasir + LIMUC
-(29,938 images; 20,836 train / 4,530 val).
-
-## ⚠️ Read this before loading
-
-**The adapter alone is not the model.** The vision encoder was trained too, and PEFT
-does not export it — `adapter_model.safetensors` contains 504 LoRA tensors and
-**zero** vision tensors. The 351 trained vision tensors (576.4 M params) are in
-`vision_tower.safetensors` and must be loaded separately, or you silently get the
-stock Qwen vision encoder and lose most of what was trained.
-
-Verified against the base checkpoint: every probed vision tensor differs
-(e.g. `blocks.26.mlp.linear_fc2.weight`, max |Δ| 0.040).
-
-## Files
-
-| file | contents |
-|---|---|
-| `adapter/` | LoRA r=32 α=64 on q/k/v/o/gate/up/down — 504 tensors, 87.3 M params |
-| `vision_tower.safetensors` | trained vision encoder, bf16 — 351 tensors, 576.4 M params |
-| `training_meta.json` | hyperparameters and the full train/val loss history |
-| `checkpoint_meta.json` | step / epoch stamp from the trainer |
-
-## Loading
-
-```python
-import torch
-from transformers import AutoProcessor, AutoModelForImageTextToText
-from safetensors.torch import load_file
-from peft import PeftModel
-
-BASE = "Qwen/Qwen3-VL-8B-Instruct"
-HERE = "."            # this folder
-
-model = AutoModelForImageTextToText.from_pretrained(BASE, dtype=torch.bfloat16, device_map="auto")
-
-# 1. trained vision encoder  (keys are already HF-native: model.visual.*)
-missing, unexpected = model.load_state_dict(load_file(f"{HERE}/vision_tower.safetensors"), strict=False)
-assert not unexpected, unexpected          # 351 keys must all land
-
-# 2. LoRA adapters on the language tower
-model = PeftModel.from_pretrained(model, f"{HERE}/adapter")
-model.eval()
-
-processor = AutoProcessor.from_pretrained(BASE, min_pixels=128*128, max_pixels=768*768)
-```
-
-`max_pixels = 768*768` caps the image at ≤576 visual tokens and matches training.
-
-## Output format
-
-The model emits **only the keys the image establishes** — absent keys are omitted,
-never returned as `null`. Targets in training carried 2, 4, 5 or 6 of these nine keys:
-
-`abnormality_present`, `abnormality_type`, `grade`, `instrument_present`,
-`landmark`, `landmark_certain`, `prep_quality`, `procedure`, `tract`
-
-```json
-{"instrument_present": true, "landmark_certain": false}
-```
-
-`instrument_present` and `landmark_certain` are always present.
-`landmark_certain: false` means the anatomy was **not annotated**, not that anatomy is absent.
-
-## Training
-
-bf16, effective batch 44 (micro 22 × accum 2), 3 epochs, 1587 steps, `adamw_8bit`.
-Two parameter groups: adapters at lr 1e-4, the full-rank vision tower at 2e-5.
-Batches are balanced round-robin across the three corpora, and 75 % of each batch
-uses a prompt that names the specific keys that sample supports.
-
-| step | train | val |
-|---|---|---|
-| 250 | 0.0143 | 0.0101 |
-| 500 | 0.0180 | 0.0104 |
-| **750** | 0.0129 | **0.0072** |
-| 1000 | 0.0076 | 0.0086 |
-| **1250** | 0.0053 | **0.0092** ← this checkpoint |
-| 1500 | 0.0059 | 0.0097 |
-
-Validation bottoms out at step 750 and rises after, while train loss keeps falling —
-the run overfits in the second half. **The step-750 checkpoint no longer exists**
-(rotated away by `keep_last_checkpoints=3`); 1250 is the lowest-validation checkpoint
-that survived.
-
-## Intended use
-
-Research artifact. Not a medical device, not validated for clinical use, and not
-evaluated on any external cohort — the three training corpora are the only data it
-has seen, and the source annotations are single-label per image.

endoedge_qwen3vl8b_lora_vis_r32/step-1250/adapter/adapter_config.json

@@ -1,50 +0,0 @@
-{
-  "alora_invocation_tokens": null,
-  "alpha_pattern": {},
-  "arrow_config": null,
-  "auto_mapping": null,
-  "base_model_name_or_path": "Qwen/Qwen3-VL-8B-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,
-  "monteclora_config": null,
-  "peft_type": "LORA",
-  "peft_version": "0.20.0",
-  "qalora_group_size": 16,
-  "r": 32,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "down_proj",
-    "gate_proj",
-    "k_proj",
-    "q_proj",
-    "v_proj",
-    "o_proj",
-    "up_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_bdlora": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false,
-  "velora_config": null
-}

endoedge_qwen3vl8b_lora_vis_r32/step-1250/adapter/adapter_model.safetensors

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endoedge_qwen3vl8b_lora_vis_r32/step-1250/checkpoint_meta.json

@@ -1,6 +0,0 @@
-{
-  "step": 1250,
-  "epoch": 0,
-  "run": "lora_vis_r32",
-  "time": 1788112823.6126652
-}

endoedge_qwen3vl8b_lora_vis_r32/step-1250/training_meta.json

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-{
-  "run": "lora_vis_r32",
-  "base_model": "Qwen/Qwen3-VL-8B-Instruct",
-  "step": 1250,
-  "total_steps": 1587,
-  "epochs": 3,
-  "effective_batch": 44,
-  "micro_batch": 22,
-  "grad_accum": 2,
-  "precision": "bf16",
-  "optimizer": "adamw_8bit",
-  "lr_adapters": 1e-4,
-  "lr_vision": 2e-5,
-  "lora": {"r": 32, "alpha": 64, "dropout": 0.05,
-           "target_modules": ["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]},
-  "trainable_params": {"adapters": 87293952, "vision_tower": 576400000, "total": 663693952},
-  "train_images": 20836,
-  "val_images": 4530,
-  "validation_loss_by_step": {
-    "250": 0.0101, "500": 0.0104, "750": 0.0072,
-    "1000": 0.0086, "1250": 0.0092, "1500": 0.0097
-  },
-  "train_loss_by_step": {
-    "250": 0.0143, "500": 0.0180, "750": 0.0129,
-    "1000": 0.0076, "1250": 0.0053, "1500": 0.0059
-  },
-  "note": "step 750 had the lowest validation loss but its checkpoint was rotated away by keep_last_checkpoints=3; 1250 is the lowest-validation checkpoint that still exists"
-}

endoedge_qwen3vl8b_lora_vis_r32/step-1250/vision_tower.safetensors

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