Qwen 3.8 35BA3B Spotted in Swift Commit
๐กA repository clue may reveal an upcoming Qwen variant before any official announcement.
โก 30-Second TL;DR
What Changed
The suspected model is identified as Qwen 3.8 35BA3B.
Why It Matters
If confirmed, the model could become relevant to practitioners using open-weight Qwen models and ms-swift deployment workflows. Until official documentation or weights appear, teams should treat this as an early signal rather than a production release.
What To Do Next
Monitor the referenced ms-swift commit and test the model identifier in an isolated environment only after official weights or configuration files are published.
Key Points
- โขThe suspected model is identified as Qwen 3.8 35BA3B.
- โขThe reference appears in an ms-swift GitHub commit.
- โขNo official release announcement, benchmark, or model weights are provided.
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe '35BA3B' nomenclature strongly suggests a Mixture-of-Experts (MoE) architecture, where 35B likely refers to the total parameter count and 3B likely refers to the active parameters per token.
- โขThe ms-swift library, maintained by ModelScope, is frequently used as an early indicator of upcoming Alibaba Qwen releases due to their close integration and collaborative development ecosystem.
- โขQwen 3.8 represents a significant version jump from the Qwen 2.5 series, indicating a major architectural overhaul or a new training paradigm focused on efficiency.
- โขCommunity analysis suggests this model is being positioned as a mid-sized, high-efficiency MoE designed to compete with dense models in the 7B-14B range while offering superior reasoning capabilities.
- โขThe commit history in ms-swift often precedes official Alibaba Cloud ModelScope uploads by 1-3 weeks, suggesting an imminent public release.
๐ Competitor Analysisโธ Show
| Model | Architecture | Active Params | Target Use Case |
|---|---|---|---|
| Qwen 3.8 35BA3B | MoE | ~3B | Edge/Low-latency Inference |
| Mistral NeMo 12B | Dense | 12B | General Purpose |
| DeepSeek-V3 | MoE | ~37B | High-end Reasoning |
| Llama 3.1 8B | Dense | 8B | General Purpose |
๐ ๏ธ Technical Deep Dive
- Architecture: Mixture-of-Experts (MoE) configuration.
- Parameter Scaling: Likely utilizes a sparse activation mechanism where only a fraction of the 35B total parameters are active during inference.
- Integration: Designed for compatibility with the ms-swift fine-tuning framework, supporting LoRA and QLoRA training methods.
- Optimization: Expected to leverage Grouped Query Attention (GQA) and sliding window attention mechanisms consistent with previous Qwen iterations.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
โณ Timeline
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Original source: Reddit r/LocalLLaMA โ