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OLLAMA LAUNCHES DEEPSEEK AND MUSE INTEGRATIONS WHILE VLLM SHIPS HARDWARE-AGNOSTIC MODELING
By RepoJournal · Filed · About Local LLMs
Ollama just opened its launch ecosystem to DeepSeek Harness and Meta's Muse Code, while vLLM pivots to HuggingFace transformers as a unified backend for tail models.
Ollama's launch system now integrates DeepSeek Harness [1] and Muse Code [2], giving developers CLI-native access to these tools with local or cloud Ollama models through a single command. The Harness integration wires web search directly into Ollama's model catalog, while Muse gets its own isolated config root to avoid collision with Meta-backed installs. Meanwhile, Ollama fixed a correctness bug in Muse Glimmer's reasoning template [3], normalizing "Reasoning effort" to "Reasoning strength" and preventing duplicate reasoning directives that could confuse the model. On the vLLM side, the project shipped a hardware-agnostic modeling layer [4] that lets tail models live in HuggingFace transformers and execute through vLLM via the transformers backend, a major shift toward reducing fragmentation across hardware. Performance gains landed across the stack: llama.cpp corrected metric counting [6] to properly track processed vs. cached tokens, SYCL added Q3_K ESIMD kernels and fused unary operations with multiplication for measurable throughput lifts [7] [8], and sglang cut diffusion denoise time by 13.1% on H100 by hoisting rope cache and fusing per-token adaLN operations [9]. Breaking security change: vLLM now enforces audio decode duration limits in NanoNemotronVL [5] to prevent resource exhaustion attacks.
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Action items
- → Review vLLM's new HF transformers backend [ref:12] if you're running tail models - it may simplify your deployment stack vllm-project/vllm [plan]
- → Update to latest ollama for DeepSeek Harness and Muse Code launch integrations [ref:1] [ref:3] ollama/ollama [plan]
- → Pull llama.cpp metric fixes [ref:6] if you depend on accurate token accounting in server mode ggml-org/llama.cpp [monitor]
- → Rebuild sglang models with latest diffusion optimizations [ref:16] for 13% latency win on H100 sgl-project/sglang [plan]
References
- [1] launch: add DeepSeek Harness integration ↗ ollama/ollama
- [2] launch: add Muse Code integration ↗ ollama/ollama
- [3] model/renderers: match Muse Glimmer reasoning template ↗ ollama/ollama
- [4] Hardware-agnostic model definition via HF transformer backend (1/N) ↗ vllm-project/vllm
- [5] fix(security): enforce audio decode duration limit in NanoNemotronVL (#50221) vllm-project/vllm
- [6] server: refactor + correctness fixes for metrics ↗ ggml-org/llama.cpp
- [7] Add DMMV ESIMD Q3_K kernel ↗ ggml-org/llama.cpp
- [8] sycl: fuse UNARY(silu|sigmoid|softplus) + MUL ↗ ggml-org/llama.cpp
- [9] [diffusion] Wan2.2-TI2V: fuse per-token adaLN table add into contiguous slices + hoist rope cache (denoise -13.1% H100 / -12.6% H200, bit-exact; eager beats compile) ↗ sgl-project/sglang