## Evaluation

```
lm_eval --model vllm-vlm --model_args pretrained=llava-hf/llava-1.5-7b-hf --tasks mmmu_val
|             Groups             |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|--------------------------------|------:|------|------|------|---|-----:|---|-----:|
|mmmu_val                        |      0|none  |      |acc   |↑  |0.2433|±  |0.0141|
| - Art and Design               |      0|none  |      |acc   |↑  |0.2250|±  |0.0384|
| - Business                     |      0|none  |      |acc   |↑  |0.2600|±  |0.0358|
| - Health and Medicine          |      0|none  |      |acc   |↑  |0.3067|±  |0.0377|
| - Humanities and Social Science|      0|none  |      |acc   |↑  |0.2667|±  |0.0403|
| - Science                      |      0|none  |      |acc   |↑  |0.1667|±  |0.0308|
| - Tech and Engineering         |      0|none  |      |acc   |↑  |0.2381|±  |0.0284|


lm_eval --model vllm-vlm --model_args pretrained=mgoin/llava-1.5-7b-hf-FP8-Dynamic --tasks mmmu_val
|             Groups             |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|--------------------------------|------:|------|------|------|---|-----:|---|-----:|
|mmmu_val                        |      0|none  |      |acc   |↑  |0.2433|±  |0.0141|
| - Art and Design               |      0|none  |      |acc   |↑  |0.2250|±  |0.0384|
| - Business                     |      0|none  |      |acc   |↑  |0.2600|±  |0.0358|
| - Health and Medicine          |      0|none  |      |acc   |↑  |0.3067|±  |0.0377|
| - Humanities and Social Science|      0|none  |      |acc   |↑  |0.2667|±  |0.0403|
| - Science                      |      0|none  |      |acc   |↑  |0.1667|±  |0.0308|
| - Tech and Engineering         |      0|none  |      |acc   |↑  |0.2381|±  |0.0284|
```

## Creation
https://github.com/vllm-project/llm-compressor/pull/185

```python
from transformers import AutoProcessor

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.sparsification import create_sparse_auto_model_class

MODEL_ID = "llava-hf/llava-1.5-7b-hf"

# Load model.
model_class = create_sparse_auto_model_class("LlavaForConditionalGeneration")
model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)

# Configure the quantization algorithm and scheme.
# In this case, we:
#   * quantize the weights to fp8 with per channel via ptq
#   * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_tower.*"],
)

# Apply quantization and save to disk in compressed-tensors format.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(processor.decode(output[0]))
print("==========================================")
```