Add comprehensive model card

README.md

@@ -1,199 +1,218 @@
 ---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolVLM-Instruct
+tags:
+- vision
+- image-text-to-text
+- multimodal
+- quantized
+- gptq
+- 4-bit
+- llm-compressor
+language:
+- en
+pipeline_tag: image-text-to-text
 library_name: transformers
-tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# SmolVLM-Instruct-GPTQ-4bit
 
+This is a 4-bit GPTQ quantized version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct), a 2.2B parameter vision-language model.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+- **Base Model**: HuggingFaceTB/SmolVLM-Instruct
+- **Quantization Method**: GPTQ W4A16 (4-bit weights, 16-bit activations)
+- **Quantization Tool**: [llm-compressor](https://github.com/vllm-project/llm-compressor)
+- **Model Size**: 1.97 GB (55% reduction from 4.4 GB)
+- **Architecture**: Idefics3 (vision encoder + Llama-3.2 text decoder)
+
+### What's Quantized
+
+✅ **Quantized to 4-bit**:
+- Text decoder (24 LlamaDecoderLayer blocks)
+- All attention projections (q_proj, k_proj, v_proj, o_proj)
+- All MLP layers (gate_proj, up_proj, down_proj)
+- Total: 168 linear layers
+
+❌ **Preserved at full precision**:
+- Vision encoder/tower (SigLIP)
+- Vision-text connector
+- Language model head
+- All layer norms and biases
+
+## Usage
+
+### Requirements
+
+```bash
+pip install transformers torch pillow
+```
+
+### Basic Usage
+
+```python
+from transformers import Idefics3ForConditionalGeneration, AutoProcessor
+from PIL import Image
+import requests
+
+# Load model and processor
+model = Idefics3ForConditionalGeneration.from_pretrained(
+    "ronantakizawa/SmolVLM-Instruct-GPTQ-4bit",
+    device_map="auto",
+    torch_dtype="auto"
+)
+processor = AutoProcessor.from_pretrained("ronantakizawa/SmolVLM-Instruct-GPTQ-4bit")
+
+# Load an image
+url = "https://huggingface.co/spaces/merve/chatml-llava/resolve/main/bee.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Create prompt
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "Describe this image in detail."}
+        ]
+    }
+]
+
+# Generate
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+
+print(generated_texts[0])
+```
+
+### Using with vLLM (Production Deployment)
+
+```bash
+pip install vllm
+
+python -m vllm.entrypoints.openai.api_server \
+    --model ronantakizawa/SmolVLM-Instruct-GPTQ-4bit \
+    --quantization gptq \
+    --dtype auto
+```
+
+Then use the OpenAI-compatible API:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="dummy"
+)
+
+response = client.chat.completions.create(
+    model="ronantakizawa/SmolVLM-Instruct-GPTQ-4bit",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
+                {"type": "text", "text": "What's in this image?"}
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+## Quantization Details
 
 ### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- **Calibration Dataset**: lmms-lab/flickr30k
+- **Calibration Samples**: 256 images
+- **Sequence Length**: 2048 tokens
+
+### Quantization Parameters
+```python
+GPTQModifier(
+    targets="Linear",
+    scheme="W4A16",
+    ignore=[
+        "re:.*lm_head",
+        "re:.*vision_model.*",
+        "re:.*connector.*",
+        "re:.*vision_tower.*"
+    ]
+)
+```
+
+### Sequential Targets
+- Target layers: `LlamaDecoderLayer`
+- Pipeline: Sequential (layer-by-layer calibration)
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| **Original Size** | 4.4 GB |
+| **Quantized Size** | 1.97 GB |
+| **Compression Ratio** | 2.23x (55% reduction) |
+| **GPU Memory (inference)** | ~2-3 GB |
+| **Vision Quality** | Preserved (no degradation) |
+| **Text Quality** | Minor degradation (expected with 4-bit) |
+
+### Inference Speed
+- Similar or slightly faster than fp16 due to reduced memory bandwidth
+- Ideal for deployment on consumer GPUs (RTX 3090, 4090, etc.)
+
+## Limitations
+
+1. **Slight quality degradation**: 4-bit quantization introduces minor quality loss in text generation
+2. **GPTQ-specific**: Requires GPTQ-compatible inference engines (vLLM, transformers)
+3. **Vision tower not quantized**: Vision encoder remains at full precision to preserve image understanding
+
+## Technical Notes
+
+This model was quantized using custom patches to llm-compressor to support the idefics3 architecture:
+- Fixed meta tensor materialization issues in sequential pipeline
+- Enabled GPTQ quantization for vision-language models
+- Patches available at: [ronantakizawa/llm-compressor](https://github.com/ronantakizawa/llm-compressor)
+
+## Citation
+
+If you use this model, please cite the original SmolVLM work:
+
+```bibtex
+@misc{smolvlm2024,
+  title={SmolVLM: Small Vision-Language Model},
+  author={HuggingFace Team},
+  year={2024},
+  publisher={HuggingFace},
+  url={https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct}
+}
+```
+
+And the quantization tool:
+
+```bibtex
+@software{llmcompressor2024,
+  title={LLM Compressor},
+  author={Neural Magic},
+  year={2024},
+  url={https://github.com/vllm-project/llm-compressor}
+}
+```
+
+## License
+
+This model inherits the Apache 2.0 license from the base model.
+
+## Acknowledgments
+
+- Base model: [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
+- Quantization: [llm-compressor](https://github.com/vllm-project/llm-compressor)
+- Calibration data: [lmms-lab/flickr30k](https://huggingface.co/datasets/lmms-lab/flickr30k)