Super-squash branch 'main' using huggingface_hub

Co-authored-by: ruke1ire <ruke1ire@users.noreply.huggingface.co>

.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
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+*.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
+assets/example.jpg filter=lfs diff=lfs merge=lfs -text
+sample_image.png filter=lfs diff=lfs merge=lfs -text
+lfm2_5_vl_1_6b_metrics.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,71 @@
+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+END OF TERMS AND CONDITIONS

README.md

@@ -0,0 +1,198 @@
+---
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- liquid
+- lfm2.5
+- lfm2
+- edge
+- vision
+base_model: LiquidAI/LFM2.5-VL-1.6B
+---
+
+<center>
+<div style="text-align: center;">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI"
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+</div>
+<div style="display: flex; justify-content: center; gap: 0.5em;">
+<a href="https://playground.liquid.ai/chat?model=lfm2.5-vl-1.6b"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+</div>
+</center>
+
+<br>
+
+# LFM2.5-VL-1.6B-Extract
+
+**LFM2.5-VL-1.6B-Extract** extracts user-defined fields from images and returns them as **JSON**. It is Liquid AI's first vision model in the [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos) collection—compact, task-specific models built for production workflows—and extends the Extract family alongside [LFM2-1.2B-Extract](https://huggingface.co/LiquidAI/LFM2-1.2B-Extract) for text documents.
+
+## ⚙️ How it works
+
+You specify what to extract as a YAML field list in the system prompt, and the model returns a JSON object with those fields. Structured outputs integrate cleanly with rule-based systems and downstream pipelines. Use it out of the box or fine-tune for domain-specific extraction.
+
+- **System prompt**:
+
+```yaml
+wood_color: The overall coloration of the wood surface
+wood_texture: The tactile quality of the wood surface 
+wood_pattern: The partern types visible on the wood surface
+```
+
+- **User prompt**:
+<img src="https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract/resolve/main/sample_image.png" width="300">
+
+- **Output**:
+```yaml
+{
+  "wood_color": "light tan to beige with darker brown streaks",
+  "wood_texture": "smooth with visible grain patterns",
+  "wood_pattern": "wavy, linear, irregular"
+}
+```
+
+Our model supports the enum feature, which lets you provide a list of possible choices alongside the field description as follows, and the model will return one of the listed values as its answer. 
+
+- **System prompt**:
+
+```yaml
+wood_color: The overall coloration of the wood surface, such as blue, red, or light tan
+wood_texture: The tactile quality of the wood surface, select from smooth, rough, or grainy
+wood_pattern: The partern types visible on the wood surface, e.g., straight, wavy, or curly
+```
+
+
+## 🌟 Use cases
+
+- Detecting safety-critical events in images (e.g. fallen person, fire, leakage) to trigger automated safety systems.
+- Collecting statistical information about objects across video frames for analytics pipelines.
+- Auto-tag product images with structured attributes for Retail/E-commerce.
+
+## 📄 Model details
+
+| Property | Detail |
+|---|---:|
+| **Parameters (LM only)** | 1.2B |
+| **Vision encoder** | SigLIP2 (~400M, [SigLIP-2 paper](https://arxiv.org/abs/2502.14786)) |
+| **Backbone layers** | hybrid conv+attention |
+| **Image input** | Single image, dynamic resolution |
+| **Context** | 128,000 tokens |
+| **Vocab size** | 65,536 (text) |
+| **Precision** | bfloat16 |
+| **License** | LFM Open License v1.0 |
+
+## 📊 Performance
+
+We evaluated LFM2.5-VL-1.6B-Extract on a 2,000-sample benchmark of
+`(image, schema, JSON)` triples, with reference labels generated by an
+ensemble of frontier multimodal models. Predictions are scored on the
+following three dimensions:
+
+- **JSON Validity** — share of samples producing strict-parseable JSON
+- **Schema Consistency F1 Score** — set-level F1 over predicted vs requested field names, macro-averaged across samples
+- **VLM Judge Score** — match against the image directly, judged by a separate vision model ([Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B))
+
+<img src="https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract/resolve/main/lfm2_5_vl_1_6b_metrics.png" width="800">
+
+| Model | Params | JSON Validity | F1 Score | VLM Judge Score |
+|---|---:|---:|---:|---:|
+| **LFM2.5-VL-1.6B-Extract** | **1.6B** | **99.6** | **99.6** | **90.6** |
+| LFM2.5-VL-1.6B | 1.6B | 91.8 | 75.8 | 66.0 |
+| FastVLM-1.5B | 1.91B | 87.3 | 80.3 | 50.9 |
+| SmolVLM2-2.2B-Instruct | 2.25B | 84.4 | 82.9 | 64.8 |
+| Qwen3.5-2B | 2.27B | 97.9 | 97.7 | 89.7 |
+| gemma-4-E2B-it | 2.3B | 97.4 | 97.1 | 84.4 |
+| InternVL3_5-2B | 2.35B | **99.6** | 99.2 | 87.7 |
+| *(ref) Qwen3-VL-4B-Instruct* | 4.44B | 99.8 | 99.7 | 92.0 |
+| *(ref) InternVL3_5-4B* | 4.73B | 99.5 | 99.4 | 90.2 |
+
+LFM2.5-VL-1.6B-Extract outperforms similarly-sized (~2B) open-source VLMs on this benchmark and is competitive with models 2× its size.
+
+**Reproducing these numbers**: The full evaluation pipeline, which includes extraction, VLM judging, and metric aggregation, is bundled in this repository under `model_eval/`. Setup, configuration, and run instructions are in the folder's [`README`](./model_eval/README.md).
+
+**Scope**: These numbers characterize the model on the input/output form it is designed for: a single input image, a YAML field list as the schema, and a flat JSON object as the output. Performance is not expected to transfer to vastly different tasks, e.g. multi-image reasoning or free-form VQA.
+
+## 🏃 How to run
+
+You can run LFM2.5-VL-1.6B-Extract with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v5.1 or newer:
+
+```bash
+pip install transformers pillow
+```
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from transformers.image_utils import load_image
+
+model_id = "LiquidAI/LFM2.5-VL-1.6B-Extract"
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    device_map="auto",
+    dtype="bfloat16",
+    trust_remote_code=True,
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+image = load_image("https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract/resolve/main/sample_image.png")
+
+fields_yaml = """wood_color: The overall coloration of the wood surface
+wood_texture: The tactile quality of the wood surface
+wood_pattern: The pattern types visible on the wood surface"""
+
+system_prompt = f"""Extract the following from the image:
+
+{fields_yaml}
+
+Respond with only a JSON object. Do not include any text outside the JSON."""
+
+conversation = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user",   "content": [{"type": "image", "image": image}]},
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+    tokenize=True,
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+response = processor.batch_decode(
+    outputs[:, inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)[0]
+print(response)
+# {
+#   "wood_color": "light tan to beige with darker brown streaks",
+#   "wood_texture": "smooth with visible grain patterns",
+#   "wood_pattern": "wavy, linear, irregular"
+# }
+```
+
+> [!WARNING]
+> The model is intended for single-turn conversations. We recommend using greedy decoding (`temperature=0`).
+
+## 📬 Contact
+
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
+
+## Citation
+
+```bibtex
+@article{liquidai2025lfm2,
+ title={LFM2 Technical Report},
+ author={Liquid AI},
+ journal={arXiv preprint arXiv:2511.23404},
+ year={2025}
+}
+```

chat_template.jinja

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+{{- bos_token -}}
+{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
+{%- set ns = namespace(system_prompt="") -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- set sys_content = messages[0]["content"] -%}
+    {%- if sys_content is not string -%}
+        {%- for item in sys_content -%}
+            {%- if item["type"] == "text" -%}
+                {%- set ns.system_prompt = ns.system_prompt + item["text"] -%}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {%- set ns.system_prompt = sys_content -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- set ns.last_assistant_index = -1 -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "assistant" -%}
+        {%- set ns.last_assistant_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message["role"] + "\n" -}}
+    {%- if message["content"] is not string -%}
+        {%- set ns.content = "" -%}
+        {%- for item in message["content"] -%}
+            {%- if item["type"] == "image" -%}
+                {%- set ns.content = ns.content + "<image>" -%}
+            {%- elif item["type"] == "text" -%}
+                {%- set ns.content = ns.content + item["text"] -%}
+            {%- else -%}
+                {%- set ns.content = ns.content + item | tojson -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- set content = ns.content -%}
+    {%- else -%}
+        {%- set content = message["content"] -%}
+    {%- endif -%}
+    {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
+        {%- if "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+    {%- endif -%}
+    {{- content + "<|im_end|>\n" -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

@@ -0,0 +1,104 @@
+{
+  "architectures": [
+    "Lfm2VlForConditionalGeneration"
+  ],
+  "bos_token_id": 1,
+  "do_image_splitting": true,
+  "downsample_factor": 2,
+  "dtype": "bfloat16",
+  "encoder_patch_size": 16,
+  "eos_token_id": 7,
+  "image_token_id": 396,
+  "max_image_tokens": 256,
+  "max_pixels_tolerance": 2.0,
+  "max_tiles": 10,
+  "min_image_tokens": 64,
+  "min_tiles": 2,
+  "model_type": "lfm2_vl",
+  "pad_token_id": 0,
+  "projector_bias": true,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 2048,
+  "projector_use_layernorm": false,
+  "text_config": {
+    "_name_or_path": "LiquidAI/LFM2-1.2B",
+    "architectures": [
+      "Lfm2ForCausalLM"
+    ],
+    "block_auto_adjust_ff_dim": true,
+    "block_dim": 2048,
+    "block_ffn_dim_multiplier": 1.0,
+    "block_mlp_init_scale": 1.0,
+    "block_multiple_of": 256,
+    "block_norm_eps": 1e-05,
+    "block_out_init_scale": 1.0,
+    "block_use_swiglu": true,
+    "block_use_xavier_init": true,
+    "bos_token_id": 1,
+    "conv_L_cache": 3,
+    "conv_bias": false,
+    "conv_dim": 2048,
+    "conv_dim_out": 2048,
+    "conv_use_xavier_init": true,
+    "dtype": "bfloat16",
+    "eos_token_id": 7,
+    "full_attn_idxs": null,
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "layer_types": [
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv"
+    ],
+    "max_position_embeddings": 128000,
+    "model_type": "lfm2",
+    "norm_eps": 1e-05,
+    "num_attention_heads": 32,
+    "num_heads": 32,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 8,
+    "pad_token_id": 0,
+    "rope_parameters": {
+      "rope_theta": 1000000.0,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "use_pos_enc": true,
+    "vocab_size": 65536
+  },
+  "tie_word_embeddings": true,
+  "tile_size": 512,
+  "transformers_version": "5.9.0",
+  "use_image_special_tokens": true,
+  "use_thumbnail": true,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip2_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 27,
+    "num_patches": 256,
+    "patch_size": 16,
+    "vision_use_head": false
+  }
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    7,
+    7
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "5.9.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0322f6fdb9f4d6f0cda585d1cd82666e09da29f44dafce21509bb5cae6a02eb8
+size 3193334216

model_eval/README.md

@@ -0,0 +1,203 @@
+# Eval pipeline (OpenRouter judge)
+
+A self-contained evaluation pipeline for LFM2.5-VL structured-extraction
+models. Extraction runs on your local GPU (vLLM/HF); the VLM judge runs
+remotely via the [OpenRouter](https://openrouter.ai/) API — no need to
+host a 30+ GB vision judge yourself.
+
+## Pipeline
+
+```
+WDS tars  ─▶  Extraction (local GPU)  ─▶  predictions
+                                              │
+              structural metrics  ◀───────────┤
+              (json validity, key P/R/F1)     │
+                                              │
+              VLM judge (OpenRouter)  ◀───────┘
+                            │
+                            ▼
+                       eval_result.json
+```
+
+Three primary metrics per run: `json_validity_rate`, `key_f1_macro`,
+`vlm_judge_score_avg` (per-key precision / recall also reported as
+diagnostic byproducts of F1).
+
+## Files
+
+```
+.
+├── README.md
+├── requirements.txt
+├── run_eval.sh       ← entry script (env vars + python call)
+├── run_eval.py       ← CLI + orchestrator + metrics aggregation
+├── extract.py        ← WDS loader + vLLM/HF extraction + JSON parsing
+├── judge.py          ← OpenRouter async VLM judging
+├── prompts/          ← 2 prompt templates (.txt)
+└── eval_data/        ← shipped 2000-sample eval set (single WDS tar)
+```
+
+Three Python files total. No nested packages, no `pyproject.toml`,
+no `pip install -e .` — just `pip install -r requirements.txt`.
+
+---
+
+## Setup
+
+### 1. Python environment
+
+```bash
+python -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+`pip install` will pull `vllm`, `torch`, `transformers`, `peft`,
+`webdataset`, `pillow`, `openai`, `tqdm`, `numpy` — ~5 GB total, takes
+5–15 min depending on the network.
+
+> **Mac / no NVIDIA GPU?** vLLM won't install. Either drop the `vllm`
+> line from `requirements.txt`, or install everything else manually and
+> run with `--extraction-backend hf` (forces the HF transformers path).
+
+### 2. OpenRouter API key
+
+Get a key from https://openrouter.ai/keys, then add it to your `~/.bashrc`:
+
+```bash
+export OPENROUTER_API_KEY=sk-or-v1-...
+```
+
+Then `source ~/.bashrc` (or open a new shell).
+
+---
+
+## Run
+
+### Quick start
+
+```bash
+bash run_eval.sh
+```
+
+Defaults:
+- Evaluates `LiquidAI/LFM2.5-VL-1.6B-Extract` on `./eval_data/`
+- Runs the full **2000 samples** (~30 min)
+- VLM judge: `qwen/qwen3.5-35b-a3b`
+- Writes results to `./eval_result.json` and log to `./eval_run.log`
+
+### Tweaking knobs
+
+Open `run_eval.sh` — every knob is a top-level variable with an inline
+comment. Common changes:
+
+```bash
+NUM_SAMPLES=50                # set 50 for a quick smoke test (~5 min)
+EXTRACTION_BACKEND="hf"       # if vLLM init fails on your machine
+EXTRACTION_BATCH=32           # bump for faster extraction (default 8)
+VLM_JUDGE_MODEL="google/gemini-2.5-flash"   # any image-capable OpenRouter model id
+JUDGE_CONCURRENCY=8           # lower if you hit OpenRouter rate limits
+```
+
+### CLI alternative
+
+If you'd rather skip the .sh wrapper, drive `run_eval.py` directly:
+
+```bash
+python run_eval.py \
+  --checkpoint-path LiquidAI/LFM2.5-VL-1.6B-Extract \
+  --data-path ./eval_data \
+  --output-path ./eval_result.json \
+  --num-samples 50 \
+  --extraction-backend auto \
+  --vlm-judge --vlm-judge-model qwen/qwen3.5-35b-a3b
+```
+
+All flags: `python run_eval.py --help`
+
+---
+
+## Eval data
+
+### What ships in `./eval_data/`
+
+2000 `(image, schema, JSON)` samples in a single WebDataset tar
+(`eval_set_n2000.tar`). Reference labels were generated by an ensemble
+of frontier multimodal models and lightly post-processed for consistency.
+
+### Bring your own
+
+Drop a `.tar` (or directory of tars) anywhere and pass
+`--data-path /path/to/your/data`.
+
+### Format spec
+
+Each sample is a WebDataset group sharing a common `<sample_id>` prefix:
+
+```
+<sample_id>.jpg                image bytes
+<sample_id>.key_explanations   JSON {key_name: description}   (the schema)
+<sample_id>.structured_text    JSON {key_name: value}         (ground truth)
+```
+
+---
+
+## Output
+
+`./eval_result.json` has three top-level keys:
+
+```jsonc
+{
+  "metadata": {
+    "checkpoint_path": "LiquidAI/LFM2.5-VL-1.6B-Extract",
+    "num_samples_evaluated": 50,
+    "extraction_backend": "auto",
+    "vlm_judge_model": "qwen/qwen3.5-35b-a3b",
+    "elapsed_s": 215.2,
+    "timestamp_utc": "2026-05-29T..."
+  },
+  "metrics": {
+    "json_validity_rate":   0.996,    // share of samples with parseable JSON
+    "key_precision_macro":  0.996,    // pred-keys ∩ gt-keys / pred-keys
+    "key_recall_macro":     0.997,
+    "key_f1_macro":         0.997,    // primary schema-consistency metric
+    "vlm_judge_score_avg":  0.922,    // 0-1, VLM scoring of all keys vs image
+    "samples_evaluated":    50
+  },
+  "samples": [
+    /* per-sample {schema, gt, prediction, per_key scores, raw judge text} */
+  ]
+}
+```
+
+The `samples[].vlm_judge_raw` field preserves the judge's verbatim text
+response — useful for debugging unexpected scores.
+
+---
+
+## Costs
+
+Default judge on a full 2000-sample run, calculated against per-token
+pricing at the time of writing (check https://openrouter.ai/models for
+current rates):
+
+| Stage | Model | Input rate | Output rate | Est. cost |
+|---|---|---|---|---|
+| VLM judge | `qwen/qwen3.5-35b-a3b` | $0.139 / 1M | $1.00 / 1M | ~$1.53 |
+
+**Full 2000-sample run: ~$1.50.** Smoke 50-sample: ~$0.04.
+
+---
+
+## Troubleshooting
+
+- **vLLM init fails** (e.g. `Ninja build failed` / `__cudaLaunch not declared`)
+  → set `EXTRACTION_BACKEND="hf"` in `run_eval.sh` for a slower-but-stable
+  fallback.
+- **OpenRouter 429 (rate limit)** → lower `JUDGE_CONCURRENCY` to 4 or 8.
+- **`No usable samples loaded`** → your tars don't have the expected
+  `<key>.jpg` / `.key_explanations` / `.structured_text` fields, or the
+  `.tar` path is wrong.
+- **A new judge model rejects with `Reasoning is mandatory`** or returns all
+  zero scores with `finish_reason=length` → edit the `_VLM_JUDGE_REASONING`
+  constant in `judge.py` (the OpenRouter `reasoning` param works differently
+  per model).

model_eval/eval_data/eval_set_n2000.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:762bc2e15c5f2636a51ba6d985e8cec31bd0658a11e48a5f6e7e4bbbd5b71923
+size 135168000

model_eval/extract.py

@@ -0,0 +1,533 @@
+"""WDS data loading, schema-aware extraction prompts, local model inference,
+and JSON-from-noise parsing — everything the trained-checkpoint stage needs.
+
+Public entry: `run_extraction(samples, model_path, backend, ...)` returns a
+list of records ready for the judge stage.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import json
+import logging
+import re
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from string import Template
+from typing import Any, Iterator, Literal
+
+import webdataset as wds
+
+logger = logging.getLogger(__name__)
+
+_IMAGE_EXTS = ("jpg", "jpeg", "png", "webp")
+_PROMPT_DIR = Path(__file__).resolve().parent / "prompts"
+_EXTRACTION_TPL = Template((_PROMPT_DIR / "extraction_system.txt").read_text(encoding="utf-8"))
+
+
+# ─── data loading ──────────────────────────────────────────────────────────
+
+
+@dataclass(frozen=True)
+class EvalSample:
+    key: str
+    image_bytes: bytes
+    schema: dict[str, str]
+    ground_truth: dict[str, object]
+
+
+def discover_tar_files(data_path: str) -> list[str]:
+    """Resolve a path/glob/brace-expansion to a sorted list of `.tar` files."""
+    if "{" in data_path and ".." in data_path:
+        expanded = list(wds.shardlists.expand_urls(data_path))
+        if expanded and Path(expanded[0]).is_dir():
+            tars: list[str] = []
+            for d in expanded:
+                if Path(d).is_dir():
+                    tars.extend(sorted(str(f) for f in Path(d).rglob("*.tar")))
+            if not tars:
+                raise FileNotFoundError(f"No .tar files found in: {data_path}")
+            return tars
+        return expanded
+
+    p = Path(data_path)
+    if p.is_file() and p.suffix == ".tar":
+        return [str(p)]
+    if p.is_dir():
+        tars = sorted(str(f) for f in p.rglob("*.tar"))
+        if not tars:
+            raise FileNotFoundError(f"No .tar files found in {data_path}")
+        return tars
+    parent = p.parent
+    tars = sorted(str(f) for f in parent.glob(p.name))
+    if not tars:
+        raise FileNotFoundError(f"No files matching pattern: {data_path}")
+    return tars
+
+
+def _first_image(sample: dict) -> bytes | None:
+    """Return the first image, preferring `imgN.jpg` order then legacy keys."""
+    multi: list[tuple[int, bytes]] = []
+    for k, v in sample.items():
+        if not isinstance(v, (bytes, bytearray)) or not k.startswith("img"):
+            continue
+        head, _, ext = k.partition(".")
+        if ext.lower() not in _IMAGE_EXTS:
+            continue
+        idx_str = head[3:]
+        if not idx_str.isdigit():
+            continue
+        multi.append((int(idx_str), bytes(v)))
+    if multi:
+        multi.sort(key=lambda x: x[0])
+        return multi[0][1]
+    for k in _IMAGE_EXTS:
+        v = sample.get(k)
+        if isinstance(v, (bytes, bytearray)):
+            return bytes(v)
+    return None
+
+
+def _decode_text(value: object) -> str:
+    if value is None:
+        return ""
+    if isinstance(value, bytes):
+        return value.decode("utf-8", errors="replace")
+    return str(value)
+
+
+def iter_eval_samples(
+    data_path: str,
+    *,
+    skip: int = 0,
+    limit: int = 0,
+) -> Iterator[EvalSample]:
+    """Yield up to `limit` EvalSamples from WDS tars.
+
+    Each sample carries `<key>.jpg`, `<key>.key_explanations` (schema with
+    descriptions), and `<key>.structured_text` (ground-truth values).
+    Samples missing image/schema/labels are silently skipped.
+    """
+    tar_files = discover_tar_files(data_path)
+    logger.info("Discovered %d tar file(s) under %s", len(tar_files), data_path)
+
+    dataset = wds.WebDataset(
+        tar_files,
+        shardshuffle=False,
+        nodesplitter=None,
+        handler=lambda e: logger.warning("WDS skip: %s", e) or True,
+    )
+    n_seen = 0
+    n_yielded = 0
+    for sample in dataset:
+        img = _first_image(sample)
+        ke = sample.get("key_explanations")
+        st = sample.get("structured_text")
+        if img is None or ke is None or st is None:
+            continue
+        try:
+            schema = json.loads(_decode_text(ke))
+            gt = json.loads(_decode_text(st))
+        except (json.JSONDecodeError, ValueError) as e:
+            logger.warning("Skip %s: bad JSON (%s)", sample.get("__key__", "?"), e)
+            continue
+        if not isinstance(schema, dict) or not isinstance(gt, dict):
+            continue
+        n_seen += 1
+        if n_seen <= skip:
+            continue
+        yield EvalSample(
+            key=str(sample.get("__key__", f"sample_{n_seen}")),
+            image_bytes=img,
+            schema=schema,
+            ground_truth=gt,
+        )
+        n_yielded += 1
+        if limit and n_yielded >= limit:
+            break
+    logger.info("Yielded %d eval sample(s) (skipped %d)", n_yielded, skip)
+
+
+# ─── prompt rendering ──────────────────────────────────────────────────────
+
+
+def schema_to_yaml(schema: dict[str, str]) -> str:
+    return "\n".join(f"{k}: {v}" for k, v in schema.items())
+
+
+def build_extraction_prompt(schema: dict[str, str]) -> str:
+    return _EXTRACTION_TPL.substitute(schema=schema_to_yaml(schema))
+
+
+# ─── JSON parsing ──────────────────────────────────────────────────────────
+
+
+def sanitize_output(text: str) -> str:
+    """Strip whitespace + markdown fences + bare `json` prefix."""
+    if not text:
+        return ""
+    s = text.strip()
+    if s.startswith("```"):
+        nl = s.find("\n")
+        s = "" if nl == -1 else s[nl + 1 :]
+        s = s.rstrip()
+        if s.endswith("```"):
+            s = s[:-3]
+        s = s.strip()
+    head = s.split("\n", 1)
+    if head and head[0].strip().lower() == "json":
+        s = head[1] if len(head) > 1 else ""
+        s = s.strip()
+    return s
+
+
+def _first_balanced(text: str, start: int) -> str | None:
+    """Return `text[start:i+1]` when braces balance; None if never balances."""
+    depth = 0
+    in_string = False
+    escape = False
+    for i in range(start, len(text)):
+        ch = text[i]
+        if escape:
+            escape = False
+            continue
+        if ch == "\\" and in_string:
+            escape = True
+            continue
+        if ch == '"':
+            in_string = not in_string
+            continue
+        if in_string:
+            continue
+        if ch == "{":
+            depth += 1
+        elif ch == "}":
+            depth -= 1
+            if depth == 0:
+                return text[start : i + 1]
+    return None
+
+
+_TRAILING_COMMA_RE = re.compile(r",(\s*[}\]])")
+# Bare empty-string entries inside an object: ` "",` or `\n  ""\n}`.
+# Some VLMs emit these as a runaway-collapse pattern.
+_BARE_EMPTY_RE = re.compile(r',\s*""\s*(?=[,}])')
+_BARE_EMPTY_BEFORE_CLOSE_RE = re.compile(r',\s*""\s*(?=\n*\s*})')
+
+
+def extract_json_strict_first(text: str) -> tuple[dict, bool]:
+    """Sanitize + parse. Returns `(dict, was_strict)`.
+
+    `was_strict=True` if the strict parse succeeded — that's what
+    `json_valid` reports. False covers repaired-success and total failure
+    (caller distinguishes via `bool(dict)`).
+    """
+    sanitized = sanitize_output(text)
+    if not sanitized:
+        return {}, False
+    start = sanitized.find("{")
+    if start == -1:
+        return {}, False
+
+    candidate = _first_balanced(sanitized, start)
+    if candidate is not None:
+        try:
+            parsed = json.loads(candidate)
+            if isinstance(parsed, dict):
+                return parsed, True
+        except (json.JSONDecodeError, ValueError):
+            pass
+
+    # Second-chance repair (ported from old bundle's `_repair_parse`):
+    # try original `bal`, then progressively repaired versions, then the
+    # last-`}` truncation with both repairs applied. First dict wins.
+    candidates: list[str] = []
+    bal = _first_balanced(sanitized[start:], 0)
+    if bal is not None:
+        candidates.append(bal)
+        c2 = _BARE_EMPTY_RE.sub("", bal)
+        c2 = _BARE_EMPTY_BEFORE_CLOSE_RE.sub("", c2)
+        candidates.append(c2)
+        candidates.append(_TRAILING_COMMA_RE.sub(r"\1", c2))
+    last_close = sanitized.rfind("}")
+    if last_close >= 0:
+        tail = sanitized[: last_close + 1]
+        candidates.append(tail)
+        tail2 = _BARE_EMPTY_RE.sub("", tail)
+        tail2 = _BARE_EMPTY_BEFORE_CLOSE_RE.sub("", tail2)
+        tail2 = _TRAILING_COMMA_RE.sub(r"\1", tail2)
+        candidates.append(tail2)
+
+    for c in candidates:
+        try:
+            parsed = json.loads(c)
+        except (json.JSONDecodeError, ValueError):
+            continue
+        if isinstance(parsed, dict):
+            return parsed, False
+    return {}, False
+
+
+# ─── extraction backends ───────────────────────────────────────────────────
+
+
+def _img_to_data_url(img_bytes: bytes) -> str:
+    b64 = base64.b64encode(img_bytes).decode("ascii")
+    return f"data:image/jpeg;base64,{b64}"
+
+
+def _build_chat_messages(schema: dict[str, str], img_bytes: bytes) -> list[dict[str, Any]]:
+    return [
+        {"role": "system", "content": build_extraction_prompt(schema)},
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": _img_to_data_url(img_bytes)}},
+            ],
+        },
+    ]
+
+
+def _extract_vllm(
+    samples: list[EvalSample],
+    *,
+    model_path: str,
+    max_model_len: int,
+    gpu_mem_util: float,
+    max_new_tokens: int,
+) -> list[str]:
+    """vLLM offline batch extraction. One shot, no retries — Ctrl+C if hung."""
+    from vllm import LLM  # type: ignore
+
+    logger.info("Initializing vLLM for %s …", model_path)
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        dtype="bfloat16",
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_mem_util,
+        enable_prefix_caching=True,
+        disable_log_stats=True,
+        limit_mm_per_prompt={"image": 1},
+    )
+    from vllm import SamplingParams  # type: ignore
+
+    sp = SamplingParams(temperature=0.0, max_tokens=max_new_tokens)
+    conversations = [_build_chat_messages(s.schema, s.image_bytes) for s in samples]
+    logger.info("vLLM.chat over %d samples …", len(samples))
+    # Suppress reasoning for extraction-side reasoning models (Qwen3 family,
+    # gpt-oss family). Without this they burn the token budget on internal
+    # <think> blocks and emit no JSON. Non-reasoning models silently ignore.
+    outputs = llm.chat(
+        conversations,
+        sampling_params=sp,
+        use_tqdm=True,
+        chat_template_kwargs={
+            "enable_thinking": False,
+            "reasoning_effort": "low",
+        },
+    )
+    texts = [o.outputs[0].text if o.outputs else "" for o in outputs]
+    return texts
+
+
+def _extract_hf(
+    samples: list[EvalSample],
+    *,
+    model_path: str,
+    max_new_tokens: int,
+    batch: int,
+) -> list[str]:
+    """HF transformers fallback. Slower but works without vLLM (e.g. Mac)."""
+    import torch  # type: ignore
+    from PIL import Image  # type: ignore
+    from transformers import AutoModelForImageTextToText, AutoProcessor  # type: ignore
+
+    logger.info("Loading HF model %s …", model_path)
+    processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+    # Decoder-only generation requires left padding so the model never sees
+    # padding tokens in the middle of the sequence at decode time.
+    if hasattr(processor, "tokenizer") and processor.tokenizer is not None:
+        processor.tokenizer.padding_side = "left"
+    model = AutoModelForImageTextToText.from_pretrained(
+        model_path,
+        dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+        trust_remote_code=True,
+        device_map="auto" if torch.cuda.is_available() else None,
+    )
+    model.eval()
+
+    outputs: list[str] = []
+    for start in range(0, len(samples), batch):
+        chunk = samples[start : start + batch]
+        msgs = [_build_chat_messages(s.schema, s.image_bytes) for s in chunk]
+        # The processor strips the image_url data URIs and replaces with PIL.
+        for m, s in zip(msgs, chunk):
+            m[1]["content"][0] = {"type": "image", "image": Image.open(io.BytesIO(s.image_bytes))}
+        inputs = processor.apply_chat_template(
+            msgs,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            # Suppress reasoning blocks (Qwen3 family) — kwarg flows into the
+            # model's Jinja chat template. Non-reasoning models ignore it.
+            enable_thinking=False,
+        ).to(model.device)
+        with torch.no_grad():
+            gen = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+        decoded = processor.batch_decode(
+            gen[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+        outputs.extend(decoded)
+        logger.info("HF extraction: %d/%d", min(start + batch, len(samples)), len(samples))
+    return outputs
+
+
+def _extract_smolvlm(
+    samples: list[EvalSample],
+    *,
+    model_path: str,
+    max_new_tokens: int,
+    max_model_len: int = 8192,
+    gpu_mem_util: float = 0.85,
+) -> list[str]:
+    """SmolVLM / Idefics3-family extraction via vLLM with user-prompt format.
+
+    Why a dedicated path:
+      - SmolVLM was trained on user/assistant turns only; system messages
+        carry weak signal and trigger generic image-captioning behavior
+        rather than schema following. So we put the schema in the *user*
+        prompt alongside the image.
+      - vLLM natively supports the Idefics3 architecture (SmolVLM v1/v2),
+        giving ~20× the throughput of single-sample HF generation. We use
+        it directly here instead of going through the generic vLLM path
+        (which would also work, but with a system-prompt template).
+    """
+    from vllm import LLM, SamplingParams  # type: ignore
+
+    logger.info("Initializing vLLM for SmolVLM/Idefics3 model: %s …", model_path)
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        dtype="bfloat16",
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_mem_util,
+        enable_prefix_caching=True,
+        disable_log_stats=True,
+        limit_mm_per_prompt={"image": 1},
+    )
+    sp = SamplingParams(temperature=0.0, max_tokens=max_new_tokens)
+
+    conversations: list[list[dict[str, Any]]] = []
+    for s in samples:
+        b64 = base64.b64encode(s.image_bytes).decode("ascii")
+        data_url = f"data:image/jpeg;base64,{b64}"
+        # User prompt (no system) — schema goes in the user turn alongside
+        # the image. This is the format SmolVLM responds to.
+        conversations.append([
+            {"role": "user", "content": [
+                {"type": "image_url", "image_url": {"url": data_url}},
+                {"type": "text", "text": build_extraction_prompt(s.schema)},
+            ]},
+        ])
+
+    logger.info("vLLM.chat over %d samples (SmolVLM) …", len(samples))
+    outputs = llm.chat(conversations, sampling_params=sp, use_tqdm=True)
+    return [o.outputs[0].text if o.outputs else "" for o in outputs]
+
+
+def _is_smolvlm(model_path: str) -> bool:
+    """Detect SmolVLM / Idefics3-family models from path."""
+    p = model_path.lower()
+    return "smolvlm" in p or "idefics" in p
+
+
+def run_extraction(
+    samples: list[EvalSample],
+    *,
+    model_path: str,
+    backend: Literal["auto", "vllm", "hf"] = "auto",
+    max_new_tokens: int = 1024,
+    max_model_len: int = 8192,
+    gpu_mem_util: float = 0.85,
+    batch: int = 8,
+) -> list[dict[str, Any]]:
+    """Run extraction; return one prediction record per input sample.
+
+    `backend="auto"` tries vLLM first and falls back to HF on import error
+    or init failure. `"vllm"` / `"hf"` force the choice.
+
+    Special case: SmolVLM / Idefics3 family always uses a dedicated code
+    path regardless of `backend` — vLLM doesn't support them well, and the
+    standard `AutoModelForImageTextToText` invocation drops the chat
+    template specifics they need.
+    """
+    if not samples:
+        return []
+
+    t0 = time.perf_counter()
+
+    # SmolVLM / Idefics: dedicated path, bypass `backend` selection.
+    if _is_smolvlm(model_path):
+        logger.info("Detected SmolVLM/Idefics-family model — using dedicated extraction path.")
+        raw_outputs = _extract_smolvlm(samples, model_path=model_path, max_new_tokens=max_new_tokens)
+        backend_used = "smolvlm"
+    elif backend == "hf":
+        raw_outputs = _extract_hf(samples, model_path=model_path, max_new_tokens=max_new_tokens, batch=batch)
+        backend_used = "hf"
+    elif backend == "vllm":
+        raw_outputs = _extract_vllm(
+            samples,
+            model_path=model_path,
+            max_model_len=max_model_len,
+            gpu_mem_util=gpu_mem_util,
+            max_new_tokens=max_new_tokens,
+        )
+        backend_used = "vllm"
+    else:  # auto
+        try:
+            raw_outputs = _extract_vllm(
+                samples,
+                model_path=model_path,
+                max_model_len=max_model_len,
+                gpu_mem_util=gpu_mem_util,
+                max_new_tokens=max_new_tokens,
+            )
+            backend_used = "vllm"
+        except Exception as e:
+            logger.warning("vLLM extraction failed (%s); falling back to HF transformers.", e)
+            raw_outputs = _extract_hf(samples, model_path=model_path, max_new_tokens=max_new_tokens, batch=batch)
+            backend_used = "hf"
+
+    dt = time.perf_counter() - t0
+    logger.info(
+        "Extraction over %d samples took %.1fs (%.2f sample/s, backend=%s).",
+        len(samples),
+        dt,
+        len(samples) / max(dt, 1e-9),
+        backend_used,
+    )
+
+    if len(raw_outputs) != len(samples):
+        raise RuntimeError(
+            f"Backend returned {len(raw_outputs)} outputs for {len(samples)} samples"
+        )
+
+    records: list[dict[str, Any]] = []
+    for s, raw in zip(samples, raw_outputs):
+        parsed, strict = extract_json_strict_first(raw)
+        records.append(
+            {
+                "key": s.key,
+                "schema": s.schema,
+                "ground_truth": s.ground_truth,
+                "prediction_raw": raw,
+                "prediction_json": parsed,
+                "prediction_strict_valid": strict,
+            }
+        )
+    return records

model_eval/judge.py

@@ -0,0 +1,296 @@
+"""OpenRouter-backed VLM judge.
+
+One prompt per sample (batched-over-keys in one JSON answer), run
+concurrently via asyncio. The VLM judge scores every key against the
+image and is the only quality signal we ship.
+
+Requires `OPENROUTER_API_KEY` in the environment. OpenRouter is
+OpenAI-compatible, so we point the `openai` SDK's `base_url` at it.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+import json
+import logging
+import os
+import re
+from pathlib import Path
+from string import Template
+from typing import Any
+
+from openai import AsyncOpenAI
+from tqdm.asyncio import tqdm_asyncio
+
+logger = logging.getLogger(__name__)
+
+_PROMPT_DIR = Path(__file__).resolve().parent / "prompts"
+_VLM_JUDGE_TPL = Template((_PROMPT_DIR / "vlm_judge_batch.txt").read_text(encoding="utf-8"))
+
+OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
+
+# Reasoning suppression for the VLM judge. Default model
+# (`qwen/qwen3.5-35b-a3b`) requires `enable_thinking=False` to avoid
+# burning the full token budget on internal thought and returning
+# `finish_reason=length` with no visible output. Both layers (top-level
+# `reasoning` + `chat_template_kwargs`) are sent so whichever the
+# underlying provider honors gets used; the other is ignored.
+_VLM_JUDGE_REASONING = {
+    "reasoning": {"enabled": False},
+    "chat_template_kwargs": {"enable_thinking": False},
+}
+
+
+# ─── per-key pre-classification ────────────────────────────────────────────
+
+
+def normalize_bool(gt_val: object, pred_val: object) -> object:
+    """If GT is bool and pred is a yes/no/true/false string, coerce."""
+    if isinstance(gt_val, bool) and isinstance(pred_val, str):
+        lower = pred_val.strip().lower()
+        if lower in ("yes", "true"):
+            return True
+        if lower in ("no", "false"):
+            return False
+    return pred_val
+
+
+def initialize_per_key_evals(records: list[dict[str, Any]]) -> None:
+    """Build a `per_key` mapping `{key: {gt, pred}}` for the VLM judge to score."""
+    for rec in records:
+        per_key: dict[str, dict[str, Any]] = {}
+        gt = rec["ground_truth"]
+        pred = rec["prediction_json"]
+        for key, gt_val in gt.items():
+            pred_val = pred.get(key)
+            if pred_val is not None:
+                pred_val = normalize_bool(gt_val, pred_val)
+            per_key[key] = {"gt": gt_val, "pred": pred_val}
+        rec["per_key"] = per_key
+
+
+# ─── prompt building ───────────────────────────────────────────────────────
+
+
+def _vlm_attr_block(entries: list[tuple[str, str, object]]) -> str:
+    lines: list[str] = []
+    for key, desc, pred in entries:
+        lines.append(f'- "{key}": {desc}\n  predicted: {json.dumps(pred, ensure_ascii=False)}')
+    return "\n".join(lines)
+
+
+def build_vlm_judge_prompt(entries: list[tuple[str, str, object]]) -> str:
+    return _VLM_JUDGE_TPL.substitute(attributes=_vlm_attr_block(entries))
+
+
+# ─── response parsing ──────────────────────────────────────────────────────
+
+
+_NUM_RE = re.compile(r"(\d+\.?\d*)")
+
+
+def _clamp(x: float) -> float:
+    return 0.0 if x < 0.0 else (1.0 if x > 1.0 else x)
+
+
+def parse_score(text: str | None) -> float:
+    if not text:
+        return 0.0
+    text = text.strip()
+    try:
+        return _clamp(float(text))
+    except ValueError:
+        m = _NUM_RE.search(text)
+        if not m:
+            return 0.0
+        try:
+            return _clamp(float(m.group(1)))
+        except ValueError:
+            return 0.0
+
+
+def parse_batch_scores(text: str | None, expected_keys: list[str]) -> dict[str, float]:
+    """Parse `{key: score}` from judge output; missing keys default to 0.0."""
+    result = {k: 0.0 for k in expected_keys}
+    if not text:
+        return result
+    # Reuse the JSON extractor from extract.py — same logic.
+    from extract import extract_json_strict_first
+
+    parsed, _ = extract_json_strict_first(text)
+    if not isinstance(parsed, dict):
+        return result
+    for k in expected_keys:
+        v = parsed.get(k)
+        if isinstance(v, bool):
+            # Defensive: bool is a subclass of int. Don't accept it as a score.
+            continue
+        if isinstance(v, (int, float)):
+            result[k] = _clamp(float(v))
+        elif isinstance(v, str):
+            # Judge sometimes returns string-formatted numbers like "0.8";
+            # parse_score extracts the leading numeric.
+            result[k] = parse_score(v)
+    return result
+
+
+def per_sample_judge_avg(per_key: dict[str, dict[str, Any]], score_field: str) -> float | None:
+    """Average a judge score across all keys of one sample. None if no scores."""
+    scores = [
+        float(v.get(score_field))
+        for v in per_key.values()
+        if isinstance(v.get(score_field), (int, float)) and not isinstance(v.get(score_field), bool)
+    ]
+    return sum(scores) / len(scores) if scores else None
+
+
+# ─── OpenRouter client + concurrent dispatch ───────────────────────────────
+
+
+def _img_to_data_url(img_bytes: bytes) -> str:
+    return f"data:image/jpeg;base64,{base64.b64encode(img_bytes).decode('ascii')}"
+
+
+def _make_client(api_key: str | None) -> AsyncOpenAI:
+    key = api_key or os.environ.get("OPENROUTER_API_KEY")
+    if not key:
+        raise RuntimeError(
+            "OPENROUTER_API_KEY is not set. Get a key at https://openrouter.ai/keys "
+            "and `export OPENROUTER_API_KEY=...` before running."
+        )
+    return AsyncOpenAI(base_url=OPENROUTER_BASE_URL, api_key=key, max_retries=3, timeout=120.0)
+
+
+async def _one_chat(
+    client: AsyncOpenAI,
+    *,
+    model: str,
+    system: str,
+    user_text: str,
+    image_bytes: bytes | None,
+    max_tokens: int,
+    semaphore: asyncio.Semaphore,
+    extra_body: dict[str, Any] | None = None,
+    max_retries_on_empty: int = 3,
+) -> str:
+    """Single OpenRouter chat call, rate-limited by `semaphore`.
+
+    Some OpenRouter providers occasionally return HTTP 200 with empty content
+    (no model output). Treating that as success silently fails the per-key
+    JSON parse — so we retry up to `max_retries_on_empty` times before
+    giving up.
+    """
+    user_content: list[dict[str, Any]] = [{"type": "text", "text": user_text}]
+    if image_bytes:
+        user_content.insert(0, {"type": "image_url", "image_url": {"url": _img_to_data_url(image_bytes)}})
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user_content},
+    ]
+    kwargs: dict[str, Any] = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": max_tokens,
+        "temperature": 0.0,
+    }
+    if extra_body:
+        kwargs["extra_body"] = extra_body
+
+    async with semaphore:
+        for attempt in range(max_retries_on_empty + 1):
+            try:
+                resp = await client.chat.completions.create(**kwargs)
+                text = resp.choices[0].message.content or ""
+                if text.strip():
+                    return text
+                finish = getattr(resp.choices[0], "finish_reason", None)
+                if attempt < max_retries_on_empty:
+                    logger.warning(
+                        "Empty response from %s (finish_reason=%s); retrying %d/%d.",
+                        model, finish, attempt + 1, max_retries_on_empty,
+                    )
+                    continue
+                logger.warning(
+                    "Empty response from %s after %d retries (finish_reason=%s); giving up.",
+                    model, max_retries_on_empty, finish,
+                )
+                return ""
+            except Exception as e:
+                logger.warning("OpenRouter call failed (%s); returning empty string.", e)
+                return ""
+        return ""
+
+
+async def _run_concurrent(
+    coros: list[Any],
+    *,
+    desc: str,
+) -> list[str]:
+    """Run coroutines concurrently with a tqdm progress bar."""
+    return await tqdm_asyncio.gather(*coros, desc=desc)
+
+
+# ─── VLM judge ─────────────────────────────────────────────────────────────
+
+
+def run_vlm_judge(
+    records: list[dict[str, Any]],
+    *,
+    sample_images: dict[str, bytes],
+    model: str,
+    max_tokens: int = 1024,
+    concurrency: int = 16,
+    api_key: str | None = None,
+) -> None:
+    """Score every key against the image via an OpenRouter VLM."""
+    plans: list[dict[str, Any]] = []
+    prompts: list[str] = []
+    imgs: list[bytes] = []
+    for rec in records:
+        per_key = rec.get("per_key", {})
+        if not per_key:
+            continue
+        keys = list(per_key.keys())
+        entries = [(k, rec["schema"].get(k, ""), per_key[k].get("pred")) for k in keys]
+        img = sample_images.get(rec["key"], b"")
+        if not img:
+            logger.warning("VLM judge: missing image for sample %s", rec["key"])
+            continue
+        prompts.append(build_vlm_judge_prompt(entries))
+        imgs.append(img)
+        plans.append({"record": rec, "keys": keys})
+
+    if not plans:
+        for rec in records:
+            rec["vlm_judge_avg"] = None
+        return
+
+    logger.info("VLM judge: scoring %d sample(s) × all keys via %s.", len(plans), model)
+
+    client = _make_client(api_key)
+    sem = asyncio.Semaphore(concurrency)
+    coros = [
+        _one_chat(
+            client,
+            model=model,
+            system="You are a meticulous visual evaluator.",
+            user_text=p,
+            image_bytes=img,
+            max_tokens=max_tokens,
+            semaphore=sem,
+            extra_body=_VLM_JUDGE_REASONING,
+        )
+        for p, img in zip(prompts, imgs)
+    ]
+    raw_outputs = asyncio.run(_run_concurrent(coros, desc="VLM judge"))
+
+    for plan, text in zip(plans, raw_outputs):
+        scores = parse_batch_scores(text, plan["keys"])
+        rec = plan["record"]
+        rec["vlm_judge_raw"] = text
+        for k in plan["keys"]:
+            rec["per_key"][k]["vlm_score"] = scores.get(k, 0.0)
+
+    for rec in records:
+        rec["vlm_judge_avg"] = per_sample_judge_avg(rec["per_key"], "vlm_score")

model_eval/prompts/extraction_system.txt

@@ -0,0 +1,5 @@
+Extract the following from the image:
+
+$schema
+
+Respond with only a JSON object. Do not include any text outside the JSON.

model_eval/prompts/vlm_judge_batch.txt

@@ -0,0 +1,17 @@
+Look at the image and evaluate whether the predicted values are correct for each attribute.
+
+Attributes (with descriptions and predictions):
+$attributes
+
+Each prediction should be an observation about the image (a word, number, or natural-language phrase like "partly cloudy" or "lying horizontally"), NOT a restatement of the description/question itself.
+
+For each attribute, rate how correct the prediction is based on what you see in the image, on a 0.0-1.0 scale:
+- 1.0: Exactly correct or semantically identical
+- 0.7-0.9: Mostly correct with minor differences
+- 0.3-0.6: Partially correct
+- 0.0-0.2: Incorrect, unrelated, or just rephrases the description instead of giving a specific value
+
+Respond with ONLY a JSON object mapping each attribute name to its score. Example:
+{"attr1": 0.9, "attr2": 0.3, "attr3": 1.0}
+
+No text outside the JSON.

model_eval/requirements.txt

@@ -0,0 +1,16 @@
+# --- Extraction (local) ---
+vllm>=0.20.1                 # optional but recommended on NVIDIA GPUs; pass
+                             # --extraction-backend hf to skip if it won't install.
+transformers>=4.45
+torch>=2.4
+peft>=0.10
+webdataset>=0.2
+pillow>=10.0
+num2words>=0.5               # SmolVLM/SmolVLM2 processor requires this for video timestamp formatting
+
+# --- OpenRouter judges (remote) ---
+openai>=1.50                 # OpenRouter is OpenAI-compatible; just point base_url at it.
+
+# --- Tiny helpers ---
+tqdm>=4.66
+numpy>=1.24

model_eval/run_eval.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+"""Eval pipeline driver: extraction → structural metrics → VLM judge → JSON.
+
+Extraction runs locally on your GPU (vLLM/HF); the VLM judge runs remotely
+via the OpenRouter API. One process, sequential stages, one JSON file out.
+"""
+
+from __future__ import annotations
+
+import argparse
+import datetime as _dt
+import json
+import logging
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+from extract import iter_eval_samples, run_extraction
+from judge import initialize_per_key_evals, run_vlm_judge
+
+
+# ─── metrics aggregation ───────────────────────────────────────────────────
+
+
+def per_sample_structural(prediction_json: dict, ground_truth: dict, strict_valid: bool) -> dict[str, Any]:
+    pred_keys = set(prediction_json.keys())
+    gt_keys = set(ground_truth.keys())
+    overlap = pred_keys & gt_keys
+    p = len(overlap) / len(pred_keys) if pred_keys else 0.0
+    r = len(overlap) / len(gt_keys) if gt_keys else 0.0
+    f1 = 2 * p * r / (p + r) if (p + r) > 0 else 0.0
+    return {
+        "json_valid": strict_valid,
+        "total_keys": len(gt_keys),
+        "total_pred_keys": len(pred_keys),
+        "overlap_keys": len(overlap),
+        "key_precision": p,
+        "key_recall": r,
+        "key_f1": f1,
+    }
+
+
+def aggregate(records: list[dict[str, Any]]) -> dict[str, Any]:
+    n = len(records)
+    if n == 0:
+        return {"samples_evaluated": 0}
+
+    def mean(xs: list[float]) -> float:
+        return sum(xs) / len(xs) if xs else 0.0
+
+    json_valid = sum(1 for r in records if r.get("json_valid"))
+    vlm_scores = [r["vlm_judge_avg"] for r in records if r.get("vlm_judge_avg") is not None]
+
+    return {
+        "json_validity_rate": json_valid / n,
+        "key_precision_macro": mean([r.get("key_precision", 0.0) for r in records]),
+        "key_recall_macro": mean([r.get("key_recall", 0.0) for r in records]),
+        "key_f1_macro": mean([r.get("key_f1", 0.0) for r in records]),
+        "vlm_judge_score_avg": mean(vlm_scores) if vlm_scores else None,
+        "samples_evaluated": n,
+    }
+
+
+def _strip_sample(rec: dict[str, Any]) -> dict[str, Any]:
+    """Drop heavy/internal fields before serialising to JSON."""
+    return {
+        "key": rec["key"],
+        "schema": rec["schema"],
+        "ground_truth": rec["ground_truth"],
+        "prediction_raw": rec["prediction_raw"],
+        "prediction_json": rec["prediction_json"],
+        "json_valid": rec.get("json_valid", False),
+        "total_keys": rec.get("total_keys", 0),
+        "total_pred_keys": rec.get("total_pred_keys", 0),
+        "key_precision": rec.get("key_precision", 0.0),
+        "key_recall": rec.get("key_recall", 0.0),
+        "key_f1": rec.get("key_f1", 0.0),
+        "vlm_judge_avg": rec.get("vlm_judge_avg"),
+        "vlm_judge_raw": rec.get("vlm_judge_raw"),
+        "per_key": rec.get("per_key", {}),
+    }
+
+
+# ─── CLI ───────────────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="OpenRouter-judged structured-extraction eval.")
+    p.add_argument("--checkpoint-path", required=True, help="HF id or local merged/LoRA dir.")
+    p.add_argument("--data-path", default="./eval_data", help="WDS tar / dir / glob.")
+    p.add_argument("--output-path", default="./eval_result.json")
+    p.add_argument("--num-samples", type=int, default=0, help="Cap N samples (0 = all).")
+    p.add_argument("--skip-samples", type=int, default=0)
+    p.add_argument("--extraction-backend", choices=["auto", "vllm", "hf"], default="auto")
+    p.add_argument("--extraction-batch", type=int, default=8)
+    p.add_argument("--extraction-max-new-tokens", type=int, default=1024)
+    p.add_argument("--extraction-gpu-mem-util", type=float, default=0.85)
+    p.add_argument("--extraction-max-model-len", type=int, default=8192)
+    p.add_argument("--vlm-judge", action=argparse.BooleanOptionalAction, default=True)
+    p.add_argument("--vlm-judge-model", default="qwen/qwen3-vl-4b-instruct")
+    p.add_argument("--vlm-judge-max-tokens", type=int, default=1024)
+    p.add_argument("--judge-concurrency", type=int, default=16, help="Concurrent OpenRouter calls.")
+    p.add_argument("--openrouter-api-key", default=None, help="Override $OPENROUTER_API_KEY.")
+    p.add_argument("--log-level", default="INFO")
+    args = p.parse_args()
+
+    logging.basicConfig(
+        level=args.log_level.upper(),
+        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    )
+
+    t_start = time.perf_counter()
+    logger = logging.getLogger("run_eval")
+    logger.info("=== OpenRouter-judged eval starting ===")
+
+    # ── load samples ─────────────────────────────────��───────────────────
+    samples = list(
+        iter_eval_samples(
+            args.data_path,
+            skip=args.skip_samples,
+            limit=args.num_samples,
+        )
+    )
+    if not samples:
+        raise RuntimeError(
+            f"No usable samples loaded from {args.data_path} — expected WDS tars "
+            "with .jpg, .key_explanations, .structured_text per sample."
+        )
+    logger.info("Loaded %d sample(s).", len(samples))
+    sample_images = {s.key: s.image_bytes for s in samples}
+
+    # ── extraction ───────────────────────────────────────────────────────
+    records = run_extraction(
+        samples,
+        model_path=args.checkpoint_path,
+        backend=args.extraction_backend,
+        max_new_tokens=args.extraction_max_new_tokens,
+        max_model_len=args.extraction_max_model_len,
+        gpu_mem_util=args.extraction_gpu_mem_util,
+        batch=args.extraction_batch,
+    )
+
+    # ── structural metrics ───────────────────────────────────────────────
+    for rec in records:
+        rec.update(
+            per_sample_structural(
+                rec["prediction_json"],
+                rec["ground_truth"],
+                rec.get("prediction_strict_valid", bool(rec["prediction_json"])),
+            )
+        )
+
+    initialize_per_key_evals(records)
+    judge_errors: dict[str, str] = {}
+
+    # ── VLM judge ────────────────────────────────────────────────────────
+    if args.vlm_judge:
+        try:
+            run_vlm_judge(
+                records,
+                sample_images=sample_images,
+                model=args.vlm_judge_model,
+                max_tokens=args.vlm_judge_max_tokens,
+                concurrency=args.judge_concurrency,
+                api_key=args.openrouter_api_key,
+            )
+        except Exception as e:
+            judge_errors["vlm_judge"] = repr(e)
+            logger.warning("VLM judge failed (%s); continuing without VLM scores.", e)
+            for rec in records:
+                rec.setdefault("vlm_judge_avg", None)
+    else:
+        for rec in records:
+            rec["vlm_judge_avg"] = None
+
+    # ── write output ─────────────────────────────────────────────────────
+    elapsed = time.perf_counter() - t_start
+    result = {
+        "metadata": {
+            "checkpoint_path": args.checkpoint_path,
+            "data_path": args.data_path,
+            "num_samples_evaluated": len(records),
+            "extraction_backend": args.extraction_backend,
+            "vlm_judge_model": args.vlm_judge_model if args.vlm_judge else None,
+            "judge_errors": judge_errors or None,
+            "elapsed_s": round(elapsed, 2),
+            "timestamp_utc": _dt.datetime.now(_dt.timezone.utc).isoformat(),
+        },
+        "metrics": aggregate(records),
+        "samples": [_strip_sample(rec) for rec in records],
+    }
+
+    out = Path(args.output_path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    print()
+    print("=== JUDGING SUMMARY ===")
+    print(f"output={out}")
+    for k, v in result["metrics"].items():
+        print(f"  {k}={v:.4f}" if isinstance(v, float) else f"  {k}={v}")
+    print(f"  elapsed_s={elapsed:.1f}")
+    print("=== JUDGING OK ===")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

model_eval/run_eval.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+# Example evaluation script — extraction on local GPU, judges via OpenRouter API.
+#   bash run_eval.sh
+
+set -euo pipefail
+
+# --- vLLM env workarounds ----------------------------------------------------
+# Needed when EXTRACTION_BACKEND=vllm or "auto". No-op on systems without
+# vLLM / environment-modules.
+#   1. CUDA toolkit on LD_LIBRARY_PATH so flashinfer's GDN/Mamba kernels can
+#      dlopen libcudart.so.12 (LFM2.5-VL is a hybrid arch and uses these).
+#   2. Skip flashinfer's sampling kernel — flashinfer 0.6 + CUDA 12.9 trigger
+#      an NVCC stub bug (`__cudaLaunch` not declared). PyTorch-native sampler
+#      is ~20% slower but works.
+#   3. Skip vLLM 0.21's DeepGEMM autotune warmup (~18 min for MoE/FP8 models).
+module load cuda12.9/toolkit/12.9.1 2>/dev/null || true
+export VLLM_USE_FLASHINFER_SAMPLER="${VLLM_USE_FLASHINFER_SAMPLER:-0}"
+export VLLM_USE_DEEP_GEMM="${VLLM_USE_DEEP_GEMM:-0}"
+
+# --- OpenRouter API key ------------------------------------------------------
+# Required. Get one at https://openrouter.ai/keys, then either:
+#   export OPENROUTER_API_KEY=...
+# in your shell, OR uncomment and set it here.
+#OPENROUTER_API_KEY="sk-or-v1-..."
+
+# --- Checkpoint --------------------------------------------------------------
+# HF id of the trained model, OR a local merged/LoRA checkpoint dir.
+CHECKPOINT="LiquidAI/LFM2.5-VL-1.6B-Extract"
+
+# --- Eval data ---------------------------------------------------------------
+# WDS tar / dir of tars / brace-glob.
+DATA_PATH="./eval_data"
+
+# Output JSON path.
+OUTPUT="./eval_result.json"
+
+# --- Sample count ------------------------------------------------------------
+# Number of samples to evaluate. Default 2000 runs the full shipped eval_data
+# (~30 min). Set to 50 for a quick smoke test (~5 min).
+NUM_SAMPLES=2000
+
+# --- Extraction (local GPU) --------------------------------------------------
+# "auto" tries vLLM first, falls back to HF transformers on init failure.
+EXTRACTION_BACKEND="auto"
+EXTRACTION_BATCH=8
+
+# --- Judge model (OpenRouter) ------------------------------------------------
+# Any image-capable OpenRouter model id works. Pricing:
+# https://openrouter.ai/models
+VLM_JUDGE_MODEL="qwen/qwen3.5-35b-a3b"
+
+# Concurrent OpenRouter calls. Lower if you hit rate limits.
+JUDGE_CONCURRENCY=16
+
+# --- Run ---------------------------------------------------------------------
+LOG_FILE="${LOG_FILE:-./eval_run.log}"
+echo "Logging to: ${LOG_FILE}"
+python run_eval.py \
+  --checkpoint-path "${CHECKPOINT}" \
+  --data-path "${DATA_PATH}" \
+  --output-path "${OUTPUT}" \
+  --num-samples "${NUM_SAMPLES}" \
+  --extraction-backend "${EXTRACTION_BACKEND}" \
+  --extraction-batch "${EXTRACTION_BATCH}" \
+  --vlm-judge --vlm-judge-model "${VLM_JUDGE_MODEL}" \
+  --judge-concurrency "${JUDGE_CONCURRENCY}" \
+  2>&1 | tee "${LOG_FILE}"

processor_config.json

@@ -0,0 +1,39 @@
+{
+  "image_processor": {
+    "data_format": "channels_first",
+    "do_image_splitting": true,
+    "do_normalize": true,
+    "do_pad": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "downsample_factor": 2,
+    "encoder_patch_size": 16,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Lfm2VlImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_image_tokens": 256,
+    "max_num_patches": 1024,
+    "max_pixels_tolerance": 2.0,
+    "max_tiles": 10,
+    "min_image_tokens": 64,
+    "min_tiles": 2,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_row_col_info": true,
+    "size": {
+      "height": 512,
+      "width": 512
+    },
+    "tile_size": 512,
+    "use_thumbnail": true
+  },
+  "processor_class": "Lfm2VlProcessor"
+}

tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_image_splitting": true,
+  "eos_token": "<|im_end|>",
+  "image_end_token": "<|image_end|>",
+  "image_start_token": "<|image_start|>",
+  "image_thumbnail": "<|img_thumbnail|>",
+  "image_token": "<image>",
+  "is_local": false,
+  "legacy": false,
+  "local_files_only": false,
+  "max_length": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "image_end_token": "<|image_end|>",
+    "image_start_token": "<|image_start|>",
+    "image_token": "<image>"
+  },
+  "pad_to_multiple_of": null,
+  "pad_token": "<|pad|>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "processor_class": "Lfm2VlProcessor",
+  "resample": 3,
+  "return_token_type_ids": false,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}