base_model: XHToken/Spark-X2.6-4B base_model_relation: quantized license: apache-2.0 library_name: gguf pipeline_tag: text-generation language:
- zh
- en tags:
- gguf
- llama-cpp
- quantization
- fit-gguf
- imatrix
- abliterated
- uncensored
- spark2_5
- dense
- reasoning
- long-context
- agent
./llama-cli -m Spark-X2.6-4B-abliterated-FIT-MINI-2G-Q3_K_M.gguf -ngl 99 -c 8192 -n 256 -p "Hello"The chat template is embedded in every file, so LM Studio, KoboldCpp, Jan and friends load them without extra configuration. Spark2.6 enables its thinking block by default — pass enable_thinking=false through your chat template if you want the answer without the reasoning trace.On context length: the upstream card and config.json declare max_position_embeddings = 1048576, and the card shows a server configured for a maximum context of 1,048,576 tokens — this repository did not measure it. It is reachable in principle because only 9 of the 36 layers are full attention (RoPE θ 5,000,000, partial RoPE 0.25) while the other 27 use a fixed 512-token sliding window, so the KV cache grows at roughly a quarter of a pure full-attention model. Start at -c 32768 and work up: on a 32 GiB card the MINI tier leaves real room for a long context, and the REFERENCE tier is mostly there to be the fidelity ceiling.