pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B
├── Merge Method: dare_ties
├── Gensyn/Qwen2.5-1.5B-Instruct
└── deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
    ├── 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/merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/resolve/main/merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.q4_k_m.gguf

# Run with llama.cpp
./main -m merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.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="merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.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 ./merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.q4_k_m.gguf' > Modelfile

# Create and run the model
ollama create merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B -f Modelfile
ollama run merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B "Your prompt here"

📋 Model Details

Original Model: pravdin/merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B
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/merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B-gguf

merged-Gensyn-Qwen2.5-1.5B-Instruct-deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B - GGUF Quantized Model

🌳 Model Tree