The evaluation methodology employed for this model utilizes a 3-tier adaptive testing system designed to systematically assess model performance across varying levels of complexity. This approach begins with a Tier 1 screening phase, consisting of 15 questions aimed at filtering out models that demonstrate non-functional capabilities. Models that pass this initial screening progress to Tier 2, which comprises 60 questions that evaluate basic competency in a more nuanced manner. Finally, Tier 3 consists of 150 questions, providing a comprehensive assessment for models that achieve a minimum accuracy threshold of 75% in Tier 2. This tiered structure allows for a focused evaluation, ensuring that only models with sufficient foundational capabilities are subjected to the more rigorous testing in Tier 3.

The testing is designed to be multi-language and distributed, allowing for a diverse range of inputs and contexts. This ensures that the model is evaluated on its ability to perform across different languages and scenarios, reflecting real-world applications.

2. Performance Progression Through Tiers

In this evaluation, the model achieved an accuracy of 43.3% (26 out of 60 questions correct) in Tier 2. Notably, Tier 1 results are not applicable (N/A) for this evaluation, indicating that the model either did not undergo this initial screening or was not assessed in this phase. The Tier 2 performance suggests that while the model possesses some basic competencies, it falls short of the expected proficiency level for further evaluation in Tier 3. The absence of results from Tier 1 and Tier 3 indicates that the model did not meet the necessary criteria to advance through the evaluation process.

3. Final Results Interpretation

The final results indicate that the model's quality is below the threshold for high-performing models. With a Tier 2 accuracy of 43.3%, the model demonstrates significant room for improvement in its understanding and processing capabilities. This level of performance suggests that the model may struggle with the complexities of language understanding and generation, which are critical for effective application in real-world scenarios. The inability to progress to Tier 3 further emphasizes the need for enhancements in the model's architecture or training data to achieve a more robust performance.

4. Comparison Context

In the context of the adaptive testing framework, a Tier 2 accuracy of 43.3% is indicative of a model that is not yet ready for deployment in applications requiring reliable performance. For comparison, models that achieve 75% or higher in Tier 2 are considered competent enough to undergo the more rigorous Tier 3 evaluation, which assesses advanced capabilities and overall robustness. The current model's performance places it in a category that may be suitable for exploratory or low-stakes applications but not for critical tasks where accuracy and reliability are paramount. This evaluation highlights the importance of iterative development and testing to enhance model performance before considering broader deployment.

In summary, while the adaptive testing approach provides a structured pathway for evaluating model capabilities, the results underscore the necessity for further refinement and training to elevate the model's performance to acceptable standards.

🌳 Model Tree

This model was created by merging the following models:

pravdin/meta-llama-Llam-Llama-3.2-3B-dare_linear
├── Merge Method: dare_ties
├── context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16
└── unsloth/Llama-3.2-3B-Instruct
    ├── density: 0.6
    ├── weight: 0.5

Merge Method: DARE_TIES - Advanced merging technique that reduces interference between models

📊 Available Quantization Formats

This repository contains multiple quantization formats optimized for different use cases:

q4_k_m: 4-bit quantization, medium quality, good balance of size and performance
q5_k_m: 5-bit quantization, higher quality, slightly larger size
q8_0: 8-bit quantization, highest quality, larger size but minimal quality loss

🚀 Usage

With llama.cpp

bash

# Download a specific quantization
wget https://huggingface.co/pravdin/meta-llama-Llam-Llama-3.2-3B-dare_linear/resolve/main/meta-llama-Llam-Llama-3.2-3B-dare_linear.q4_k_m.gguf

# Run with llama.cpp
./main -m meta-llama-Llam-Llama-3.2-3B-dare_linear.q4_k_m.gguf -p "Your prompt here"

With Python (llama-cpp-python)

python

from llama_cpp import Llama

# Load the model
llm = Llama(model_path="meta-llama-Llam-Llama-3.2-3B-dare_linear.q4_k_m.gguf")

# Generate text
output = llm("Your prompt here", max_tokens=512)
print(output['choices'][0]['text'])

With Ollama

bash

# Create a Modelfile
echo 'FROM ./meta-llama-Llam-Llama-3.2-3B-dare_linear.q4_k_m.gguf' > Modelfile

# Create and run the model
ollama create meta-llama-Llam-Llama-3.2-3B-dare_linear -f Modelfile
ollama run meta-llama-Llam-Llama-3.2-3B-dare_linear "Your prompt here"

📋 Model Details

Original Model: pravdin/meta-llama-Llam-Llama-3.2-3B-dare_linear
Quantization Tool: llama.cpp
License: Same as original model
Use Cases: Optimized for local inference, edge deployment, and resource-constrained environments

🎯 Recommended Usage

q4_k_m: Best for most use cases, good quality/size trade-off
q5_k_m: When you need higher quality and have more storage/memory
q8_0: When you want minimal quality loss from the original model

⚡ Performance Notes

GGUF models are optimized for:

Faster loading times
Lower memory usage
CPU and GPU inference
Cross-platform compatibility

For best performance, ensure your hardware supports the quantization format you choose.

This model was automatically quantized using the Lemuru LLM toolkit.

pravdin/meta-llama-Llam-Llama-3.2-3B-dare_linear-gguf

meta-llama-Llam-Llama-3.2-3B-dare_linear - GGUF Quantized Model

Evaluation Summary for Model Card

1. Adaptive Testing Approach