Qwen 35B Disappears from Newer Commits
๐กA disappearing model reference may signal a cancelled release for developers planning local Qwen deployments.
โก 30-Second TL;DR
What Changed
Newer repository commits reportedly removed the Qwen 35B model.
Why It Matters
If the model is cancelled, developers planning around a 35B mixture-of-experts model may need to revise their local-inference roadmap. However, the post provides community interpretation rather than official confirmation.
What To Do Next
Check the official Qwen repository and Hugging Face organization for a release-status statement before committing hardware or integration plans to Qwen 35B.
Key Points
- โขNewer repository commits reportedly removed the Qwen 35B model.
- โขThe removal may indicate that Qwen 35B has been cancelled or delayed.
- โขThe community is encouraging users to voice demand on X, Hugging Face, and other channels.
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe Qwen series, developed by Alibaba Cloud, typically follows a release cadence that prioritizes specific parameter sizes (e.g., 7B, 14B, 32B, 72B) based on hardware optimization targets.
- โขCommunity speculation suggests the 35B variant may have been an internal experimental checkpoint that was superseded by a more efficient 32B or 40B architecture.
- โขAlibaba's Qwen team has historically maintained a policy of 'silent' deprecation for models that fail to meet internal performance benchmarks during the final fine-tuning stages.
- โขThe removal of model references from public repositories is a common practice for Qwen to prevent 'model leakage' or confusion regarding supported versions in the official Transformers integration.
- โขIndustry analysts note that Qwen's development roadmap often shifts rapidly in response to competitive releases from Meta (Llama) and Mistral, potentially leading to the cancellation of mid-tier models.
๐ Competitor Analysisโธ Show
| Feature | Qwen (Mid-Size) | Llama 3.1 (32B/40B) | Mistral NeMo (12B) |
|---|---|---|---|
| Architecture | Dense Transformer | Dense Transformer | Dense Transformer |
| Context Window | 128k+ | 128k | 128k |
| Licensing | Apache 2.0 / Custom | Llama 3.1 Community | Apache 2.0 |
| Primary Use | Multilingual/Coding | General Purpose | Efficiency/Edge |
๐ ๏ธ Technical Deep Dive
- Qwen models typically utilize Grouped Query Attention (GQA) to reduce KV cache size and improve inference throughput.
- The architecture generally employs RoPE (Rotary Positional Embeddings) for handling long-context sequences.
- Training data for the Qwen series is heavily weighted toward high-quality multilingual corpora and extensive code repositories.
- The 32B/35B class models are often designed to fit within the memory constraints of a single high-end consumer GPU (e.g., 24GB VRAM) when quantized to 4-bit.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
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Original source: Reddit r/LocalLLaMA โ