---

base_model: novita/kimi-k2.7-code-dspark

tags:

• speculators
• safetensors
• speculative-decoding
• dspark
• text-generation
• custom_code
• base_model:moonshotai/Kimi-K2.7-Code
• base_model:finetune:moonshotai/Kimi-K2.7-Code
• region:us

---

Kimi-K2.7-Code DSpark GGUF

GGUF quantizations of novita DSpark draft model for Kimi-K2.7-Code.

Use with BeeLlama.cpp, a llama.cpp fork with advanced quantization features.

---

Kimi-K2.7-Code DSpark speculator

Overview

A DSpark speculator model for the Kimi-K2.7-Code base model, enabling faster

inference through speculative decoding. DSpark extends the DFlash parallel draft

backbone with two lightweight heads: a Markov logit-bias head (low-rank

intra-block token dependency) and a per-position confidence head (accept-rate

prediction). This checkpoint was trained in the Camelot-Ray online pipeline,

where the draft consumes hidden states streamed from a live Kimi-K2.7-Code vLLM

server.

This export is from Camelot exp38 checkpoint 3.

Model Specifications

• Base Model: Kimi-K2.7-Code
• Format: Safetensors (single-file bf16, 6.3 GB, 44 tensors)
• Draft: 3 layers (Qwen3-style GQA), hidden 7168, 56 heads / 8 KV heads,

head_dim 128, FFN 18432, rope_theta 50000, block_size=8

• Vocabulary: pruned draft vocab 32,000 (d2t/t2d remap tables shipped in the

weights), target vocab 163,840; mappings reused from the Kimi-K2.6/K2.7

compatible tokenizer setup

• DSpark heads: Markov rank 256 (vanilla), confidence head (with-markov),

mask_token_id=163608

• Aux hidden-state layers: [1, 29, 57]
• Trained context: seq 20000

Evaluation Results

Online vLLM nightly spec-decode, greedy decoding, TP=8, Kimi-K2.7-Code verifier,

max_model_len=20000, cudagraphs enabled, and

fuse_allreduce_rms=false.

The table also includes Novita's public Eagle3-MLA draft

novita/kimi-k2.7-code-eagle3-mla under the same Kimi-K2.7-Code verifier,

TP=8, cudagraph, and fusion-off serving setup. Cells show

tok/s / speedup / accept_len. The standard rows use 6 prompts per benchmark;

code-extra rows use the full LiveCodeBench and SPEED-Bench coding manifests

with max_tokens=512.

| benchmark | rows | baseline tok/s | DSpark n=3 | DSpark n=7 | Novita Eagle3 n=3 | Novita Eagle3 n=7 | best |

| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |

| gsm8k | 6 | 132.0 | 282.2 / 2.14x / 2.937 | 309.1 / 2.34x / 3.659 | 281.7 / 2.13x / 2.941 | 277.3 / 2.10x / 3.595 | DSpark n=7 |

| math500 | 6 | 132.0 | 317.1 / 2.40x / 3.249 | 367.4 / 2.78x / 4.303 | 288.6 / 2.19x / 3.026 | 294.5 / 2.23x / 3.851 | DSpark n=7 |

| aime | 6 | 131.5 | 276.8 / 2.10x / 2.778 | 318.4 / 2.42x / 3.716 | 263.2 / 2.00x / 2.766 | 275.3 / 2.09x / 3.626 | DSpark n=7 |

| humaneval | 6 | 132.1 | 285.1 / 2.16x / 2.875 | 336.6 / 2.55x / 3.953 | 285.9 / 2.17x / 3.029 | 291.8 / 2.21x / 3.850 | DSpark n=7 |

| livecodebench | 121 | 129.8 | 227.5 / 1.75x / 2.306 | 231.0 / 1.78x / 2.696 | 219.5 / 1.69x / 2.342 | 198.5 / 1.52x / 2.593 | DSpark n=7 |

| speedbench_coding | 80 | 131.2 | 282.0 / 2.15x / 2.837 | 303.7 / 2.31x / 3.530 | 272.1 / 2.06x / 2.886 | 281.6 / 2.13x / 3.693 | DSpark n=7 |

Use DSpark with num_speculative_tokens=7 as the default for code, math, and

most reasoning traffic.

Serving with vLLM

Requires a vLLM nightly with DSpark support:

uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly

vllm serve moonshotai/Kimi-K2.7-Code \
    --tensor-parallel-size 8 \
    --max-model-len 20000 \
    --trust-remote-code \
    --compilation-config='{"pass_config": {"fuse_allreduce_rms": false}}' \
    --speculative-config '{
        "model": "novita/kimi-k2.7-code-dspark",
        "num_speculative_tokens": 7,
        "method": "dspark"
    }'

Known vLLM-nightly caveats, with workarounds:

1. Draft-side FA3 AOT scheduling can crash with `scheduler_metadata must have

shape (metadata_size)` because the GPU-worker spec-decode path misses

fast_build=True when building draft attention metadata. Patch

vllm/v1/worker/gpu/spec_decode/speculator.py and

vllm/v1/worker/gpu/attn_utils.py to pass fast_build=True.

1. CUDA-graph capture can fail with a flashinfer allreduce workspace-size error

under spec-decode token expansion; disable the fusion:

--compilation-config='{"pass_config": {"fuse_allreduce_rms": false}}'.

Training Details

• Initialization: continued from the Kimi-K2.7-Code DSpark exp37 checkpoint
• Data: Kimi-K2.7-Code training mix with public Kimi-MTP data and hidden

states streamed from the live Kimi-K2.7-Code verifier; seq 20000

• Steps: 20000 optimizer steps
• Schedule: lr 3e-4 cosine, warmup 300, global batch 8, accumulation 2
• Loss: 0.1 CE + 0.9 TV over block-diffusion anchors, decay_gamma 4.0,

max_anchors 3072

• Semantics: apply_verifier_norm=False, hidden_states = concat of aux

layers [1, 29, 57]