Instructions to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anbeeld/Kimi-K2.7-Code-DSpark-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anbeeld/Kimi-K2.7-Code-DSpark-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Ollama
How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with Ollama:
```
ollama run hf.co/Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with Docker Model Runner:
```
docker model run hf.co/Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M
```

Lemonade

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-DSpark-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Anbeeld/Kimi-K2.7-Code-DSpark-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Anbeeld/Kimi-K2.7-Code-DSpark-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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:

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.
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]

Downloads last month: 132

GGUF

Model size

3B params

Architecture

dflash

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Anbeeld/Kimi-K2.7-Code-DSpark-GGUF

Base model

moonshotai/Kimi-K2.7-Code

Finetuned

novita/kimi-k2.7-code-dspark

Quantized

(1)

this model

Collection including Anbeeld/Kimi-K2.7-Code-DSpark-GGUF

DSpark

Collection

GGUF quantizations of DSpark speculative-decoding draft models (DFlash backbone + Markov head). • 20 items • Updated about 3 hours ago