README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

Patched LLama 3.2 8B from LLaMA 3.2 11B Model

Here’s the complete, refined code for patching the weights:
```python
# Import required libraries
from transformers import AutoProcessor, AutoTokenizer, AutoModelForImageTextToText, AutoModelForCausalLM

# Load the 11B Vision-Instruct model
processor = AutoProcessor.from_pretrained("meta-llama/Llama-3.2-11B-Vision-Instruct")
model = AutoModelForImageTextToText.from_pretrained("meta-llama/Llama-3.2-11B-Vision-Instruct")

# Load the 8B text-only model
s_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
s_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

# Prepare input text for testing
input_text = "Write me a poem about Machine Learning."
input_ids = s_tokenizer(input_text, return_tensors="pt")

# Test the original 8B model
outputs = s_model.generate(**input_ids, do_sample=False, max_new_tokens=10)
print("8B Model Output:", s_tokenizer.decode(outputs[0]))

# Patch weights from the 11B model into the 8B model
model_weight = model.state_dict()
s_model_dict = s_model.state_dict()
skip_layer = 0  # Track skipped layers

for key in s_model_dict.keys():
    if "layers." in key:
        layer_idx = int(key.split("layers.")[1].split(".")[0])  # Extract layer index
        try:
            s_model_dict[key] = model_weight[
                "language_model." + key.replace(f"layers.{layer_idx}.", f"layers.{layer_idx + skip_layer}.")
            ]
        except KeyError:
            skip_layer += 1
            s_model_dict[key] = model_weight[
                "language_model." + key.replace(f"layers.{layer_idx}.", f"layers.{layer_idx + skip_layer}.")
            ]
    else:
        s_model_dict[key] = model_weight["language_model." + key]

# Test the patched 8B model
outputs = s_model.generate(**input_ids, do_sample=False, max_new_tokens=10)
print("Patched 8B Model Output:", s_tokenizer.decode(outputs[0]))

# Test the original 11B model
outputs = model.generate(**input_ids, do_sample=False, max_new_tokens=10)
print("11B Model Output:", s_tokenizer.decode(outputs[0]))

```

### **Example Outputs**

**Prompt:** "Write me a poem about Machine Learning."

**Outputs:**
1. **8B Model Output (Before Patching):**
   ```
   <|begin_of_text|>Write me a poem about Machine Learning.
   Artificial minds, born from code,
   Learning
   ```

2. **Patched 8B Model Output:**
   ```
   <|begin_of_text|>Write me a poem about Machine Learning.
   In silicon halls, where data reigns
   ```

3. **11B Model Output:**
   ```
   <|begin_of_text|>Write me a poem about Machine Learning.
   In silicon halls, where data reigns
   ```

---

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