The evaluation methodology employed for this model utilizes a 3-tier adaptive testing system designed to assess model performance progressively. This approach begins with a Tier 1 screening phase, consisting of 15 questions aimed at filtering out models that are completely non-functional. Models that pass this initial screening advance to Tier 2, which comprises 60 questions that evaluate basic competency across a range of tasks. Finally, models that achieve a minimum accuracy threshold of 75% in Tier 2 are eligible for Tier 3, a comprehensive evaluation consisting of 150 questions that rigorously tests the model's capabilities in a more demanding context.

This adaptive testing framework is particularly effective in multi-language and distributed testing environments, as it allows for a tailored assessment that can accommodate diverse linguistic and contextual challenges. By structuring the evaluation in tiers, we can efficiently allocate resources and focus on models that demonstrate potential, while also ensuring that only the most capable models undergo the most intensive testing.

2. Performance Progression Through Tiers

In this evaluation, the model achieved an accuracy of 40.0% (24 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 reflects a basic competency assessment, where the model's ability to handle a variety of tasks was tested.

Given that the model did not reach the 75% accuracy threshold required to progress to Tier 3, it indicates that while the model possesses some functional capabilities, it does not meet the criteria for high-performing models that would undergo deeper evaluation.

3. Final Results Interpretation

The final results of 40.0% accuracy in Tier 2 suggest that the model demonstrates limited competency in the evaluated tasks. This level of performance indicates that the model may struggle with certain aspects of the tasks presented, which could be attributed to various factors such as insufficient training data, inadequate model architecture, or challenges in understanding the nuances of the questions posed.

While the model is not deemed non-functional, its performance suggests that significant improvements are necessary before it can be considered reliable for practical applications. The results highlight areas for potential enhancement, including further training, fine-tuning, or adjustments to the model's architecture.

4. Comparison Context

In the context of the adaptive testing framework, a 40.0% accuracy score in Tier 2 is below the expected performance level for models that are intended for deployment in real-world applications. Typically, models achieving 75% or higher in Tier 2 are considered competent and are eligible for the more rigorous Tier 3 evaluation, which assesses their performance under more challenging conditions.

For comparison, models that score in the range of 60% to 75% in Tier 2 may still be viable candidates for further development, while those scoring below 60% often require substantial revisions before they can be effectively utilized. Therefore, this model's performance indicates that it is currently not suitable for high-stakes applications and necessitates further refinement to enhance its accuracy and reliability.

In summary, while the model has demonstrated some level of functionality, its performance in Tier 2 underscores the need for targeted improvements to elevate its competency to meet the standards expected of high-performing AI models.

🌳 Model Tree

This model was created by merging the following models:

pravdin/meta-I-Hermes-3-dare_linear
├── Merge Method: dare_ties
├── context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16
└── NousResearch/Hermes-3-Llama-3.2-3B
    ├── 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-I-Hermes-3-dare_linear/resolve/main/meta-I-Hermes-3-dare_linear.q4_k_m.gguf

# Run with llama.cpp
./main -m meta-I-Hermes-3-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-I-Hermes-3-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-I-Hermes-3-dare_linear.q4_k_m.gguf' > Modelfile

# Create and run the model
ollama create meta-I-Hermes-3-dare_linear -f Modelfile
ollama run meta-I-Hermes-3-dare_linear "Your prompt here"

📋 Model Details

Original Model: pravdin/meta-I-Hermes-3-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-I-Hermes-3-dare_linear-gguf

meta-I-Hermes-3-dare_linear - GGUF Quantized Model

Evaluation Summary for Model Card

1. Adaptive Testing Approach