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drowzeys/keys-Qwen3.8-27B-DSpark-vLLM-YaRN-1M-Context
keys-Qwen3.8-27B-DSpark-vLLM-YaRN-1M-Context is a machine learning model from drowzeys. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Config-only fork of Doopeworld/Qwen3.8-27B-DSpark-vLLM (= byte-identical RadixArk/Qwen3.8-27B-DSpark weights).
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From the Hugging Face model README
Config-only fork of Doopeworld/Qwen3.8-27B-DSpark-vLLM
(= byte-identical RadixArk/Qwen3.8-27B-DSpark weights).
Stock DSpark is native 262,144. 1M on Qwen3.8 is YaRN factor 4 on the target.
SGLang/vLLM then copy that override into the draft; the draft is a flat Qwen3
config, so YaRN either crashes (text_config / max_position_embeddings) or
the draft rope stays at 262k while the target is at 1M.
This checkpoint does for DSpark what Keys already did for DFlash2
(dflash2_config_yarn_1m.json): put the 1M YaRN rope on the draft.
- "max_position_embeddings": 262144
+ "max_position_embeddings": 1048576
"rope_parameters": {
- "factor": 32.0,
- "original_max_position_embeddings": 8192,
+ "factor": 4.0,
+ "original_max_position_embeddings": 262144,
"rope_type": "yarn",
"rope_theta": 10000000
}
Architectures stay Qwen3DSparkModel so vLLM does not route this to DeepSeek-V4.
Bind this config.json over the draft. Keep the target YaRN hf-overrides.
Do not also inject nested text_config into the draft.
--max-model-len 1048576 \
--hf-overrides '{"text_config":{"max_position_embeddings":1048576,"rope_parameters":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144,"rope_theta":10000000}}}' \
--speculative-config '{"method":"dspark","model":"/drafter","num_speculative_tokens":7,"draft_sample_method":"probabilistic"}'
This is a rope overlay, not a retrain. Acceptance at 1M is unmeasured until a 1M target + this draft is benched.
Weights and DSpark method: RadixArk / SpecForge. vLLM architecture rename: Doopeworld.
YaRN-1M draft config: Keys (drowzeys).