release

.gitattributes

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+*.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
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+# vrthinker
+
+A video reward model that compares two videos against a text prompt and outputs per-dimension preferences:
+**TA** (Text Alignment), **MQ** (Motion Quality), **VQ** (Visual Quality), **OA** (Overall).
+Each label is one of `1` (Video 1 wins), `2` (Video 2 wins), `0` (tie).
+
+The model reasons step-by-step and may call a `select_frames` tool to request additional frames from the videos
+before committing to an answer.
+
+## Install
+
+```bash
+pip install torch transformers accelerate pillow opencv-python
+```
+
+## Inference
+
+Save the snippet below as `infer.py`, then:
+
+```bash
+python infer.py --video1 path/to/v1.mp4 --video2 path/to/v2.mp4 \
+                --prompt "A robot rides a unicorn across a rainbow bridge."
+```
+
+```python
+# infer.py
+import argparse, json, re
+from pathlib import Path
+
+import cv2
+import torch
+from PIL import Image
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+MODEL_DIR = str(Path(__file__).resolve().parent)        # the dir containing this README
+FRAMES_PER_VIDEO = 128
+INITIAL_PER_VIDEO = 4
+MAX_TURNS = 6
+MAX_FRAMES_PER_CALL = 12
+IMAGE_SIDE = 448
+
+SYSTEM_PROMPT = """Task Description:
+Your task is to compare two videos generated based on the same text prompt by analyzing their frames in detail and provide an overall judgment along with a judgment for each evaluation dimension.
+
+The provided frames are downsampled from these videos:
+- Video 1: First four input frames.
+- Video 2: Next four input frames.
+
+Evaluation Dimensions:
+1. Text Alignment (TA): How faithfully each video reflects the text prompt.
+2. Visual Quality (VQ): Aesthetics, artifacts, blurriness, distortion, color, resolution, flickering.
+3. Motion Quality (MQ): Smoothness, jitter, unnatural motion, temporal consistency.
+4. Overall Assessment (OA): Holistic judgment across the above.
+
+Frames and Analysis Rules:
+- 8 sampled frames are provided initially (4 per video), evenly downsampled from 128 frames per video. The first 4 are Video 1, the next 4 are Video 2.
+- Each video has 128 frames (indices 0-127). To inspect more frames, call select_frames with the indices you need; the tool retrieves the same indices from both videos symmetrically.
+- Tool returns are paired: for [i, j, k] you get (v1[i], v2[i], v1[j], v2[j], v1[k], v2[k]). Use this pairing to compare the same moment across both videos.
+- Each tool call accepts at most 12 indices.
+
+Format Requirement:
+1. <Snapshot></Snapshot> — summarize useful visual details after receiving frames.
+2. <Think></Think> — reasoning.
+3. <Answer></Answer> — final judgment.
+
+Label semantics: 1 = Video 1 better, 2 = Video 2 better, 0 = tie.
+
+Examples:
+<Answer>TA=1, VQ=1, MQ=0, OA=1</Answer>
+
+Tool call format:
+When you want to inspect more frames, emit a tool call inside <tool_call></tool_call> tags:
+<tool_call>{"name": "select_frames", "arguments": {"frame_indices": [10, 30, 60, 90]}}</tool_call>
+"""
+
+
+def extract_frames(video_path: str, indices: list[int]) -> list[Image.Image]:
+    """Return PIL frames at the given indices, evenly mapped over the video's actual length."""
+    cap = cv2.VideoCapture(video_path)
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    out: list[Image.Image] = []
+    for idx in indices:
+        # map idx in [0, FRAMES_PER_VIDEO) -> real frame in [0, total)
+        real = min(int(idx / FRAMES_PER_VIDEO * total), total - 1)
+        cap.set(cv2.CAP_PROP_POS_FRAMES, real)
+        ok, frame = cap.read()
+        if not ok:
+            continue
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame).resize((IMAGE_SIDE, IMAGE_SIDE))
+        out.append(img)
+    cap.release()
+    return out
+
+
+def initial_frames(v1: str, v2: str) -> list[Image.Image]:
+    idxs = [int(FRAMES_PER_VIDEO * (i + 0.5) / INITIAL_PER_VIDEO) for i in range(INITIAL_PER_VIDEO)]
+    return extract_frames(v1, idxs) + extract_frames(v2, idxs)
+
+
+def tool_frames(v1: str, v2: str, indices: list[int]) -> list[Image.Image]:
+    indices = indices[:MAX_FRAMES_PER_CALL]
+    out: list[Image.Image] = []
+    for i in indices:
+        out += extract_frames(v1, [i])
+        out += extract_frames(v2, [i])
+    return out
+
+
+def parse_tool_call(text: str) -> dict | None:
+    m = re.search(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", text, re.DOTALL)
+    if not m:
+        return None
+    try:
+        obj = json.loads(m.group(1))
+        return obj.get("arguments", {})
+    except json.JSONDecodeError:
+        return None
+
+
+def parse_answer(text: str) -> dict | None:
+    m = re.search(r"<Answer>(.*?)</Answer>", text, re.DOTALL | re.IGNORECASE)
+    if not m:
+        return None
+    body = m.group(1)
+    return {d: int(re.search(rf"{d}\s*=\s*(\d)", body).group(1))
+            for d in ("TA", "MQ", "VQ", "OA")
+            if re.search(rf"{d}\s*=\s*(\d)", body)}
+
+
+@torch.inference_mode()
+def run(video1: str, video2: str, prompt: str) -> dict:
+    processor = AutoProcessor.from_pretrained(MODEL_DIR, trust_remote_code=True)
+    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+        MODEL_DIR, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+    ).eval()
+
+    images = initial_frames(video1, video2)
+    user_text = (
+        f"Compare the two videos generated from the following prompt and evaluate them "
+        f"across Text Alignment (TA), Motion Quality (MQ), Visual Quality (VQ), and "
+        f"Overall Assessment (OA).\n\nPrompt: {prompt}\n\n"
+        f"The first 4 images are uniformly sampled from Video 1, and the next 4 are from "
+        f"Video 2. Each video has 128 frames (indices 0-127). "
+        f"Use the select_frames tool to request additional frames if needed."
+    )
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
+        {"role": "user", "content": [{"type": "image"}] * len(images)
+                                    + [{"type": "text", "text": user_text}]},
+    ]
+
+    for turn in range(MAX_TURNS):
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = processor(text=[text], images=images, return_tensors="pt", padding=True).to(model.device)
+        output_ids = model.generate(**inputs, max_new_tokens=2048, do_sample=False, temperature=0.0)
+        reply = processor.batch_decode(
+            output_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
+        )[0]
+
+        messages.append({"role": "assistant", "content": [{"type": "text", "text": reply}]})
+
+        answer = parse_answer(reply)
+        if answer:
+            return answer
+
+        call = parse_tool_call(reply)
+        if not call or "frame_indices" not in call:
+            return parse_answer(reply) or {"TA": None, "MQ": None, "VQ": None, "OA": None}
+
+        new_imgs = tool_frames(video1, video2, call["frame_indices"])
+        images += new_imgs
+        messages.append({
+            "role": "user",
+            "content": [{"type": "image"}] * len(new_imgs)
+                       + [{"type": "text",
+                           "text": f"<tool_response>Retrieved {len(call['frame_indices'])} "
+                                   f"frame pairs ({call['frame_indices']}) symmetrically from both "
+                                   f"videos.</tool_response>"}],
+        })
+
+    return {"TA": None, "MQ": None, "VQ": None, "OA": None}
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--video1", required=True)
+    p.add_argument("--video2", required=True)
+    p.add_argument("--prompt", required=True)
+    args = p.parse_args()
+    print(json.dumps(run(args.video1, args.video2, args.prompt), indent=2))
+```
+
+## Output
+
+```
+{
+  "TA": 1,
+  "MQ": 0,
+  "VQ": 2,
+  "OA": 1
+}
+```
+
+`1` = Video 1 wins on that dimension, `2` = Video 2 wins, `0` = tie.
+
+## Hardware
+
+Requires ~16 GB GPU memory in bf16. Tested on a single A100/H100.

added_tokens.json

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+  "<tool_call>": 151657,
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chat_template.jinja

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+{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
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+  "initializer_range": 0.02,
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+  "max_position_embeddings": 128000,
+  "max_window_layers": 28,
+  "model_type": "qwen2_5_vl",
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+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "text_config": {
+    "architectures": [
+      "Qwen2_5_VLForConditionalGeneration"
+    ],
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+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
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+      "full_attention",
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+      "rope_type": "default",
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+    "use_cache": true,
+    "use_sliding_window": false,
+    "vision_token_id": 151654,
+    "vocab_size": 152064
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "depth": 32,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
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+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "in_channels": 3,
+    "in_chans": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 3420,
+    "model_type": "qwen2_5_vl",
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+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "window_size": 112
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+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 152064
+}

generation_config.json

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+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 1e-06,
+  "transformers_version": "4.57.6"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
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+    "<|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|>"
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+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+{
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+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}

video_preprocessor_config.json

@@ -0,0 +1,43 @@
+{
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+  "data_format": "channels_first",
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+  "device": null,
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+  "max_frames": 768,
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+  "merge_size": 2,
+  "min_frames": 4,
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+  "num_frames": null,
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+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
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+  "temporal_patch_size": 2,
+  "video_metadata": null,
+  "video_processor_type": "Qwen2VLVideoProcessor"
+}