Qwen2.5-1.5B-dare_linear-merge - GGUF Quantized Model

This is a collection of GGUF quantized versions of pravdin/Qwen2.5-1.5B-dare_linear-merge.

Evaluation Summary for Model Card

1. Adaptive Testing Approach

The evaluation of the model was conducted using a 3-tier adaptive testing system designed to assess model performance progressively. This methodology allows for a structured assessment that filters out non-functional models while providing a comprehensive evaluation of those that demonstrate basic competency.

Tier 1 serves as a quick screening phase, consisting of 15 questions aimed at identifying models that do not meet minimum operational standards. Models that fail this tier are excluded from further evaluation.
Tier 2 comprises a medium assessment with 60 questions, focusing on evaluating the model's basic competency across various tasks. This tier is critical for determining whether a model can proceed to the more rigorous Tier 3 evaluation.
Tier 3 is a deep evaluation phase that includes 150 questions, designed for high-performing models that achieve at least 75% accuracy in Tier 2. This tier assesses the model's capabilities in greater depth, ensuring a thorough understanding of its performance across diverse scenarios.

The adaptive nature of this testing approach allows for a tailored evaluation process, where only models demonstrating sufficient competency advance to more challenging assessments.

2. Performance Progression Through Tiers

The model in question was evaluated at the Tier 2 level, achieving an accuracy of 48.3% (29 out of 60 questions correct). Unfortunately, the model did not progress to Tier 3, as it did not meet the minimum threshold of 75% accuracy required for deeper evaluation.

The absence of results from Tier 1 indicates that the model successfully passed the initial screening, confirming its basic functionality. However, the performance in Tier 2 suggests that while the model is operational, it struggles with the complexity of the tasks presented, indicating areas for improvement.

3. Final Results Interpretation

The final results indicate that the model's quality is below the expected standard for Tier 2 assessments. An accuracy of 48.3% suggests that the model has significant limitations in its ability to understand and respond to the questions posed. This level of performance may reflect issues such as inadequate training data, insufficient model architecture, or challenges in generalizing across the diverse question types included in the evaluation.

Given that Tier 2 is designed to assess basic competency, the model's performance raises concerns about its readiness for practical applications, particularly in multi-language and distributed testing environments where robustness and adaptability are crucial.

4. Comparison Context

In the context of adaptive testing, a score of 48.3% in Tier 2 is indicative of a model that is not yet ready for deployment in real-world scenarios. For comparison, models that achieve 75% or higher in Tier 2 are considered competent enough to undergo Tier 3 evaluations, which are essential for high-stakes applications.

The results suggest that this model may require further refinement and retraining, particularly in areas where it demonstrated weaknesses during the assessment. Continuous improvement efforts should focus on enhancing the model's understanding of the task requirements and its ability to generalize across different languages and contexts, which are critical for success in distributed testing environments.

In summary, while the model has passed the initial screening, its performance in Tier 2 highlights significant gaps that must be addressed before it can be considered for advanced evaluation or practical deployment.

🌳 Model Tree

This model was created by merging the following models:

pravdin/Qwen2.5-1.5B-dare_linear-merge
├── Merge Method: dare_ties
├── Gensyn/Qwen2.5-1.5B-Instruct
└── Qwen/Qwen2.5-1.5B-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

# Download a specific quantization
wget https://huggingface.co/pravdin/Qwen2.5-1.5B-dare_linear-merge/resolve/main/Qwen2.5-1.5B-dare_linear-merge.q4_k_m.gguf

# Run with llama.cpp
./main -m Qwen2.5-1.5B-dare_linear-merge.q4_k_m.gguf -p "Your prompt here"

With Python (llama-cpp-python)

from llama_cpp import Llama

# Load the model
llm = Llama(model_path="Qwen2.5-1.5B-dare_linear-merge.q4_k_m.gguf")

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

With Ollama

# Create a Modelfile
echo 'FROM ./Qwen2.5-1.5B-dare_linear-merge.q4_k_m.gguf' > Modelfile

# Create and run the model
ollama create Qwen2.5-1.5B-dare_linear-merge -f Modelfile
ollama run Qwen2.5-1.5B-dare_linear-merge "Your prompt here"

📋 Model Details

Original Model: pravdin/Qwen2.5-1.5B-dare_linear-merge
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.