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Model: voidful/Llama-3.2-8B-Instruct
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---
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
```
---
## 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]
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### Model Sources [optional]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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).
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