voidful/Llama-3.2-8B-Instruct

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:

# 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

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]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]