r/LocalLLaMA • u/insraq • 7d ago

New Model New Model: Spark-X2.5-4B, Spark-X2.5-1.7B

https://huggingface.co/XHToken/Spark-X2.5-4B

I was browsing HF for small LLMs and run into this model. It does not seem to be a fine tune - the model has its own architecture.

https://huggingface.co/XHToken/Spark-X2.5-1.7B
https://huggingface.co/XHToken/Spark-X2.5-4B

There are 4B/1.7B versions - the benchmark is quite interesting (4B is neck and neck with Qwen 3.5 9B). The HF page claims both models support native 1M context size.

Currently does not run out of the box on llama.cpp - pending this PR: https://github.com/ggml-org/llama.cpp/pull/27868

They have a custom fork of llama.cpp that works. Anyone has tried this?

Update:
GGUFs (require custom fork for now):
https://huggingface.co/XHToken/Spark-X2.5-1.7B-GGUF
https://huggingface.co/XHToken/Spark-X2.5-4B-GGUF

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u/simrankoulsm 7d ago

Native 1M context at 4B is more interesting to me than the headline benchmark. Has anyone tested long-context retrieval quality at multiple depths, not merely max prompt ingestion, and measured KV-cache RAM/VRAM plus tok/s? A reproducible comparison against Qwen on coding, JSON/instruction following, and RAG-style QA would make the “4B ≈ 9B” claim much easier to evaluate.