---
base_model:
- openbmb/MiniCPM-V-2_6
---

## Creation

```python
from transformers import AutoProcessor, AutoModelForCausalLM

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot, wrap_hf_model_class

MODEL_ID = "openbmb/MiniCPM-V-2_6"

# Load model.
model_class = wrap_hf_model_class(AutoModelForCausalLM)
model = model_class.from_pretrained(MODEL_ID, torch_dtype="auto", trust_remote_code=True).to("cuda")
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

# 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:resampler.*", "re:vpm.*"],
)

# 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, trust_remote_code_model=True)
processor.save_pretrained(SAVE_DIR)
```