Qwen15-DeepSeek-Coder-Merge
This is a merge of pre-trained language models created using MergeKit, combining the foundational capabilities of Qwen 1.5 with DeepSeek Coder's programming expertise through an efficient SLERP fusion.
About Me
I'm David Soeiro-Vuong, a third-year Computer Science student working as an apprentice at TW3 Partners, a company specialized in Generative AI. Passionate about artificial intelligence and language models optimization, I focus on creating efficient model merges that balance performance and capabilities.
Merge Details
Merge Method
This model uses SLERP (Spherical Linear Interpolation) with carefully tuned parameters to achieve optimal performance balance:
- Weighted Blend: t=0.6 provides a slightly stronger influence from the DeepSeek Coder model
- Complete Layer Merging: Full layer-range coverage ensures comprehensive knowledge transfer
- Format: bfloat16 precision for efficient memory usage
Models Merged
- Qwen/Qwen1.5-7B-Chat - Alibaba's Qwen 1.5 chat model known for its strong conversational capabilities and instruction following
- deepseek-ai/deepseek-coder-6.7b-instruct - DeepSeek's specialized coding model with excellent programming language understanding and code generation abilities
Configuration
slices:
- sources:
- model: Qwen/Qwen1.5-7B-Chat
layer_range: [0, 32]
- model: deepseek-ai/deepseek-coder-6.7b-instruct
layer_range: [0, 32]
merge_method: slerp
base_model: Qwen/Qwen1.5-7B-Chat
parameters:
t: 0.6
dtype: bfloat16
Model Capabilities
This merge combines:
- Qwen 1.5's strong instruction following and general knowledge capabilities
- DeepSeek Coder's specialized programming expertise and code generation abilities
- Enhanced technical understanding and explanation capabilities
- Fully open architecture with no usage restrictions
The resulting model provides enhanced performance on tasks requiring both conversational fluency and programming expertise, such as:
- Code generation across multiple programming languages
- Technical documentation and explanations
- Algorithm implementation and problem-solving
- Software development assistance with natural language understanding
- Debugging and code optimization suggestions
Limitations
- Inherits limitations from both base models
- May exhibit inconsistent behavior for certain advanced programming tasks
- No additional alignment or fine-tuning beyond the base models' training
- Model was created through parameter merging without additional training data
- Slight model size mismatch (7B vs 6.7B) may introduce some parameter interpolation artifacts
License
This model is released under the Apache 2.0 license, consistent with the underlying models' licenses.
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