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Model: prithivMLmods/Hoags-2B-Exp Source: Original Platform
2026-05-21 01:12:12 +08:00
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+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- text-generation-inference
+- Qwen
+- Hoags
+---
+![sdefsed.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/hpDw305N-pMouN0EiuJYL.png)
+
+> [!WARNING]
+> **Note:**  This model contains artifacts and may perform poorly in some cases.
+
+# **Hoags-2B-Exp**
+
+The **Hoags-2B-Exp** model is a fine-tuned version of Qwen2-VL-2B-Instruct, specifically designed for reasoning tasks, context reasoning, and multi-modal understanding. If you ask for an image description, it will automatically describe the image and answer the question in a conversational manner.
+
+# **Key Enhancements**
+
+* **Advanced Contextual Reasoning**: Hoags-2B-Exp achieves state-of-the-art performance in reasoning tasks by enhancing logical inference and decision-making.
+
+* **Understanding images of various resolution & ratio**: The model excels at visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
+
+* **Long-Context Video Understanding**: Capable of processing and reasoning over videos of 20 minutes or more for high-quality video-based question answering, content creation, and dialogue.
+
+* **Device Integration**: With strong reasoning and decision-making abilities, the model can be integrated into mobile devices, robots, and automation systems for real-time operation based on both visual and textual input.
+
+* **Multilingual Support**: Supports text understanding in various languages within images, including English, Chinese, Japanese, Korean, Arabic, most European languages, and Vietnamese.
+
+# **Demo Inference**
+
+![demo.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/43w_tJW1-q93qHVegMhIX.png)
+
+# **How to Use**
+
+```python
+instruction = "Analyze the image and generate a clear, concise description of the scene, objects, and actions. Respond to user queries with accurate, relevant details derived from the visual content. Maintain a natural conversational flow and ensure logical consistency. Summarize or clarify as needed for understanding."
+```
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# Load the model with automatic device placement
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Hoags-2B-Exp", torch_dtype="auto", device_map="auto"
+)
+
+# Recommended: Enable flash_attention_2 for better performance in multi-image and video tasks
+# model = Qwen2VLForConditionalGeneration.from_pretrained(
+#     "prithivMLmods/Hoags-2B-Exp",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# Load processor
+processor = AutoProcessor.from_pretrained("prithivMLmods/Hoags-2B-Exp")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Analyze the context of this image."},
+        ],
+    }
+]
+
+# Prepare input
+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
+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)
+```
+
+# **Buffer Handling**
+```python
+buffer = ""
+for new_text in streamer:
+    buffer += new_text
+    buffer = buffer.replace("<|im_end|>", "")
+    yield buffer
+```
+
+# **Key Features**
+
+1. **Advanced Contextual Reasoning:**  
+   - Optimized for **context-aware problem-solving** and **logical inference**.
+
+2. **Optical Character Recognition (OCR):**  
+   - Extracts and processes text from images with exceptional accuracy.
+
+3. **Mathematical and Logical Problem Solving:**  
+   - Supports complex reasoning and outputs equations in **LaTeX format**.
+
+4. **Conversational and Multi-Turn Interaction:**  
+   - Handles **multi-turn dialogue** with enhanced memory retention and response coherence.
+
+5. **Multi-Modal Inputs & Outputs:**  
+   - Processes images, text, and combined inputs to generate insightful analyses.
+
+6. **Secure and Efficient Model Loading:**  
+   - Uses **Safetensors** for faster and more secure model weight handling.