---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
datasets:
- unsloth/Radiology_mini
---

# Uploaded  model

- **Developed by:** MMoshtaghi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
- **Finetuned on dataset:** [unsloth/Radiology_mini](https://huggingface.co/datasets/unsloth/Radiology_mini)
- **PEFT method :** [Quantized LoRA](https://huggingface.co/papers/2305.14314)

## Quick start

```python
from datasets import load_dataset
from unsloth import FastVisionModel

model, tokenizer = FastVisionModel.from_pretrained(
    model_name = "MMoshtaghi/Llama-3.2-11B-Vision-LoRAAdpt-Radiology",
    load_in_4bit = True,
)
FastVisionModel.for_inference(model) # Enable for inference!

dataset = load_dataset("unsloth/Radiology_mini", split = "train")
image = dataset[0]["image"]
instruction = "You are an expert radiographer. Describe accurately what you see in this image."

messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": instruction}
    ]}
]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)
inputs = tokenizer(
    image,
    input_text,
    add_special_tokens = False,
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model_inf.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
                   use_cache = True, temperature = 1.5, min_p = 0.1)
```

### Framework versions

- TRL: 0.13.0
- Transformers: 4.47.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
- Unsloth: 2025.1.5

## Citations
This VLM model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.