pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct-gguf

merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct - GGUF Quantized Model

This is a collection of GGUF quantized versions of pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct.

🌳 Model Tree

This model was created by merging the following models:

pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct
├── 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/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct/resolve/main/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct.q4_k_m.gguf

# Run with llama.cpp
./main -m merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct.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="merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct.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 ./merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct.q4_k_m.gguf' > Modelfile

# Create and run the model
ollama create merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct -f Modelfile
ollama run merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct "Your prompt here"

📋 Model Details

• Original Model: pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-Qwen-Qwen2.5-1.5B-Instruct
• 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.

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This model was automatically quantized using the Lemuru LLM toolkit.