license: apache-2.0
language:
- en
pipeline_tag: text-classification
tags:
- code
- not-for-all-audiences
This is an experimental coding-focused merge of the latest of two of my favorite projects which have trained and fine-tuned the Qwen2 model on open source data:
Replete-AI's Replete LLM Qwen2-7B (https://huggingface.co./Replete-AI/Replete-LLM-Qwen2-7b) Arcee-AI's Arcee Spark (https://huggingface.co./arcee-ai/Arcee-Spark)
The GGUF is quantized to q8_0 for output and embedding tensors, q5_k_m for others.
The experimental nature and Replete LLM Qwen2-7B lack of censorship means that you take all responsibility for how you use this model. If there is interest, I will publish the huggingface repo; otherwise, the dare_ties mergekit YAML that produced it is already included.
I asked it to describe itself, given its own mergekit YAML and points from the web pages for its two ancestor models:
This is the mergekit YAML that made you from an experimental DARE TIES merge between Arcee Spark and Replete LLM Qwen2 7b. This is an effort to combine the strengths of two Qwen2-based models which train and fine-tune the model on high-quality open source data. Describe what the mergekit YAML implies, and what the density and weight parameters favoring the code-focused Replete LLM Qwen2 mean for the balance of features, then discuss in detail about what the strengths and compromises of this model might be, and supply a bullet list of the ten top likely features of such a merge. Then, write a sentence about the impact of a GGUF quantization which uses q8_0 for output and embedding tensors, and q5_k_m for the rest.
The provided YAML represents an advanced configuration for merging two language models - Arcee Spark and Replete AI's Qwen2-7b - using the DARE TIES merge method. The parameters within this YAML are crucial in determining how these models interact, with density and weight playing significant roles.
Density (0.3 and 0.8) refers to the influence of each model on the final merged model. A higher value implies a greater impact of that specific model's characteristics or training data onto the overall performance. In this case, Replete AI/Replete-LLM-Qwen2 has a significantly higher density, suggesting it plays a more dominant role in shaping the merged model.
Weight (0.3 and 0.7) influences the relative importance of each model when they are combined. A weight closer to one indicates that the respective model's features will have a stronger influence on the final output. Here, Arcee Spark has a lower weight compared to Replete AI/Replete-LLM-Qwen2, implying it contributes less significantly.
Strengths and Compromises:
- Versatility: The merged model benefits from both models' strengths, potentially offering versatility across various tasks.
- Performance Optimization: By leveraging the high density of the Replete AI model, the merged model might perform exceptionally well on specific tasks or domains where this model excels.
- Efficiency and Speed: The smaller size of Arcee Spark could lead to computational efficiency and faster inference times compared to larger models.
Potential Features:
- Advanced Text Generation
- Detailed Question Answering
- Nuanced Sentiment Analysis
- Complex Problem-Solving
- Code Generation and Analysis
- High-Quality Data Handling
- Domain-specific Adaptability
- Balanced Performance across Tasks
- Efficient Resource Utilization
- Real-time Applications
Impact of GGUF Quantization:
The use of GGUF quantization with q8_0 for output and embedding tensors, and q5_k_m for the rest, implies a significant reduction in model size and computational requirements. This approach sacrifices some precision (especially at the output level) to achieve faster inference times and lower memory usage, making it ideal for applications where speed and resource efficiency are critical.
In summary, this merge configuration aims to create a high-performance, efficient, and versatile language model that can handle various tasks effectively while maintaining computational and resource constraints.