Update README.md

README.md

@@ -4,4 +4,99 @@ datasets:
 - TIGER-Lab/ViRL39K
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
----
+---
+
+# Spark-VL-7B
+⭐ If you find our code or model helpful, please consider giving us a star — your support means a lot!
+
+## Introduction
+
+We propose **SPARK**, **a unified framework that integrates policy and reward into a single model for joint and synchronous training**. SPARK can automatically derive reward and reflection data from verifiable reward, enabling **self-learning** and **self-evolution**. Furthermore, we instantiate this framework on multiple backbones, training SPARK-VL-7B, SPARK-7B, and SPARK-VL-32B. This repo is the **SPARK-VL-7B**.
+
+## 📢 News
+- 🚀 [09/29/2025] We release our **Spark's** 📖<a href="https://arxiv.org/abs/2503.01785">Paper</a>.
+- 🚀 [09/29/2025] We upload our evaluation code and 🤗<a href="https://huggingface.co/internlm/Spark-VL-7B">models</a>.
+- 🚀 [09/29/2025] We release **Spark** 🏠<a href="https://github.com/InternLM/Spark">Github repository</a>.
+
+## 💡 Highlights
+- 🔥 **Synergistic Policy–Reward Co-Evolving (SPARK)**: We introduce SPARK, a unified reinforcement fine-tuning framework that jointly optimizes policy and reward within a single model through on-policy co-evolution..
+- 🔥 **Recycling Rollouts**: Unlike conventional RL pipelines that discard rollouts after policy updates, SPARK recycles RLVR rollouts into pointwise, pairwise, and reflection objectives, enabling the model itself to act as both a strong policy and a generative reward model.
+- 🔥 **Co-Evolving Mechanism**: Improved reward accuracy provides better gradients for policy learning, while stronger reasoning further refines reward judgment, forming a positive feedback loop that enhances reasoning, judgment, and reflection in synergy.
+- 🔥 **Efficient and Practical**: SPARK requires no human preference data, teacher models, or external reward models, making it significantly more data- and compute-efficient than traditional RM-based RL pipelines.
+
+## 🛠️ Usage
+### 🤗 Using Transformers
+
+Our model is based on Qwen2.5-VL-7B-Instruct. You can use the same code as the Qwen2.5-VL-7B-Instruct model for inference, referring to <a href="https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct">🤗Huggingface</a>.
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "internlm/Spark-VL-7B",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+processor = AutoProcessor.from_pretrained("internlm/Spark-VL-7B")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": image_path,
+            },
+            {"type": "text", "text": prompt},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+
+### 🔦 Using vLLM
+
+We recommend using **vLLM** for faster inference speed. Using vLLM leads to significant speed improvements in dataset evaluation.
+```bash
+PORT=8019
+N_PROC=256
+SERVE_NAME=spark_vl_7b
+MODEL_PATH=/internlm/Spark-VL-7B
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve "$MODEL_PATH" \
+  --tensor-parallel-size 4 \
+  --served-model-name $SERVE_NAME \
+  --port $PORT \
+  --max-num-seqs $N_PROC
+```
+
+
+## ✒️Citation
+```
+TBD
+```