Instructions to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Anbeeld/Qwen3.8-27B-DFlash2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Anbeeld/Qwen3.8-27B-DFlash2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M

SGLang

How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Anbeeld/Qwen3.8-27B-DFlash2-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anbeeld/Qwen3.8-27B-DFlash2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Anbeeld/Qwen3.8-27B-DFlash2-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anbeeld/Qwen3.8-27B-DFlash2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with Ollama:
```
ollama run hf.co/Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with Docker Model Runner:
```
docker model run hf.co/Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M
```

Lemonade

How to use Anbeeld/Qwen3.8-27B-DFlash2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Anbeeld/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-DFlash2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Anbeeld/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-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/Qwen3.8-27B-DFlash2-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"

Qwen 3.8 27B DFlash2 GGUF

GGUF quantizations of Inco AI DFlash2 draft model for Qwen 3.8 27B.

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

Benchmark results

Machine configuration

OS: Windows 11 Pro
CPU: AMD Ryzen 9 9950X 16-Core Processor
GPU: NVIDIA GeForce RTX 3090
Server CPU thread pool: 16 threads

Test setup

Runtime: BeeLlama.cpp v0.4.4, CUDA 13.1
Target model: Qwen3.8-27B-UD-Q4_K_M
15 generations per each prompt + quant pair
DFlash2 setup: 7 draft tokens per block
Context: 8192 tokens
Batch size 4096, micro-batch size 1024
Temperature 1.0, top-p 0.95, top-k 20
Reasoning disabled

Task store module

Show prompt

Write one complete Python 3 file using only the standard library.

Return only Python code. Do not use markdown, comments, tests, examples, or explanatory text.

Implement a deterministic Task store module with a compact, repetitive structure that is easy to predict.

Required shape:
- imports: dataclasses, datetime, typing
- dataclass Task with fields id: int, title: str, status: str, created_at: str
- class TaskStore with an internal dict[int, Task]
- methods: add, get, rename, mark_done, reopen, delete, clear, list_all, list_open, list_done, count_open, count_done, titles, to_dicts, __len__, __contains__
- add assigns increasing integer ids starting at 1
- valid statuses are "open" and "done"
- all list methods return tasks sorted by id
- count_open and count_done use explicit loops
- titles returns task titles sorted by task id
- to_dicts returns deterministic dictionaries sorted by id
- to_dicts includes id, title, status, and created_at keys for every task
- raise ValueError for empty title or missing task id
- use straightforward if statements and explicit loops
- keep method bodies short and similar in style
- no argparse, no JSON, no file IO, no unittest, no pytest
- target about 110 to 132 lines of code
- define __all__ = ["Task", "TaskStore"]
- stop immediately after defining __all__

Quant type	Size (MB)	Mean length	Median tok/s	Mean tok/s	Median AR	Mean AR
Baseline	n/a	897.93	42.222	42.237	n/a	n/a
Q2_K	705.43	891.20	109.128	109.079	0.912	0.913
Q3_K_M	916.70	891.20	108.330	108.747	0.916	0.915
Q4_K_M	1,143.01	893.00	108.123	107.906	0.910	0.908
Q5_K_M	1,359.93	899.87	107.408	107.332	0.906	0.907
Q6_K	1,590.41	906.80	106.738	107.123	0.895	0.900
Q8_0	2,056.41	890.93	109.767	109.067	0.916	0.911
bf16	3,860.29	899.47	108.508	108.468	0.915	0.914

Key-value report module

Show prompt

Write one complete Python 3 file using only the standard library.

Return only Python code. Do not use markdown, comments, tests, examples, or explanatory text.

Implement a deterministic KV report module with a compact, repetitive structure that is easy to predict.

Required shape:
- imports: dataclasses, typing
- dataclass Row with fields key: str, value: str
- class Report with an internal list[Row]
- methods: add, set, get, delete, clear, keys, values, items, sorted_rows, render_lines, render_text, render_csv, filter_prefix, update_many, to_dict, copy, count_prefix, first_key, __len__, __contains__
- add appends a new row and rejects duplicate keys
- set updates an existing row or appends a new row
- get returns the value for a key
- delete removes a row by key
- keys, values, and items preserve insertion order
- sorted_rows returns rows sorted by key
- render_lines returns strings formatted as "key: value"
- render_text joins render_lines with newline characters
- render_csv returns deterministic "key,value" lines with a header
- filter_prefix returns a new Report containing keys that start with the prefix
- update_many applies set for each key and value in a dictionary sorted by key
- to_dict returns a deterministic dictionary sorted by key
- copy returns a new Report with the same rows in the same order
- count_prefix returns the number of keys that start with the prefix using an explicit loop
- first_key returns the first key and raises ValueError when there are no rows
- raise ValueError for empty keys, duplicate keys, or missing keys
- use straightforward if statements and explicit loops
- keep method bodies short and similar in style
- no enum, no alignment modes, no markdown table, no textwrap, no itertools, no unittest, no pytest
- target about 130 to 155 lines of code
- define __all__ = ["Row", "Report"]
- stop immediately after defining __all__

Quant type	Size (MB)	Mean length	Median tok/s	Mean tok/s	Median AR	Mean AR
Baseline	n/a	1,026.60	41.865	41.829	n/a	n/a
Q2_K	705.43	980.80	105.876	105.453	0.888	0.886
Q3_K_M	916.70	1,007.53	105.543	105.158	0.885	0.883
Q4_K_M	1,143.01	1,002.20	106.475	105.659	0.895	0.890
Q5_K_M	1,359.93	1,003.00	104.483	104.082	0.886	0.883
Q6_K	1,590.41	1,022.80	107.050	106.483	0.900	0.894
Q8_0	2,056.41	992.40	108.184	108.047	0.893	0.895
bf16	3,860.29	1,001.47	103.355	103.295	0.874	0.878

Qwen3.8-27B-DFlash2

Blog | GitHub

This repository contains the DFlash 2 draft model for Qwen/Qwen3.8-27B. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoint is also mirrored at z-lab/Qwen3.8-27B-DFlash2.

DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution.

DFlash 2: parallel block drafting with a candidate path selector

Quick Start

Serve with SGLang:

pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git#subdirectory=python"

python -m sglang.launch_server \
  --model-path Qwen/Qwen3.8-27B \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 \
  --speculative-num-draft-tokens 8

Or with vLLM:

pip install -U "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/52816/head"

vllm serve Qwen/Qwen3.8-27B \
  --speculative-config '{
    "method": "dflash",
    "model": "incoai/Qwen3.8-27B-DFlash2",
    "num_speculative_tokens": 7
  }'

See the blog post for other engines and more details.

Evaluation

Runtime: SGLang on one NVIDIA H200, with FlashAttention 3 for target and draft attention
Speculation block size: 8 (7 draft tokens per verification step)
Sampling: Qwen3.8's officially recommended parameters (temperature 1.0, top-p 0.95, top-k 20), with xhigh reasoning effort
Maximum new tokens: 4096
Prompts: benchmark formatting from z-lab/dflash

We compare autoregressive decoding, Qwen3.8's built-in seven-token MTP, a community DSpark drafter (RadixArk/Qwen3.8-27B-DSpark), and DFlash 2. All speculative methods propose seven draft tokens per verification step.

Acceptance Length

Acceptance length is the per-request mean of completion tokens divided by verification steps. Higher is better.

Task	MTP	DSpark	DFlash 2
GSM8K	5.02	4.36	5.46
MATH-500	4.72	3.92	5.28
HumanEval	3.91	3.30	4.39
MBPP	3.99	3.51	4.79
MT-Bench	3.74	3.01	4.10

Throughput

Throughput is total output tokens divided by end-to-end wall time. Each cell shows output tok/s (speedup vs. autoregressive).

Concurrency 1

Task	Autoregressive	MTP	DSpark	DFlash 2
GSM8K	68.9	178.5 (2.59×)	185.3 (2.69×)	236.1 (3.43×)
MATH-500	69.0	172.8 (2.51×)	174.5 (2.53×)	230.7 (3.34×)
HumanEval	69.0	151.9 (2.20×)	159.9 (2.32×)	214.6 (3.11×)
MBPP	69.0	153.1 (2.22×)	163.3 (2.37×)	226.9 (3.29×)
MT-Bench	68.9	134.9 (1.96×)	137.6 (2.00×)	184.0 (2.67×)

Concurrency 8

Task	Autoregressive	MTP	DSpark	DFlash 2
GSM8K	467.2	1,022.1 (2.19×)	1,040.8 (2.23×)	1,328.7 (2.84×)
MATH-500	480.0	1,023.5 (2.13×)	1,025.8 (2.14×)	1,368.3 (2.85×)
HumanEval	483.4	934.2 (1.93×)	956.5 (1.98×)	1,291.5 (2.67×)
MBPP	478.0	938.1 (1.96×)	974.1 (2.04×)	1,328.0 (2.78×)
MT-Bench	480.5	835.2 (1.74×)	802.3 (1.67×)	1,090.2 (2.27×)

Concurrency 32

Task	Autoregressive	MTP	DSpark	DFlash 2
GSM8K	1,329.8	1,381.1 (1.04×)	1,506.5 (1.13×)	1,922.5 (1.45×)
MATH-500	1,505.8	1,415.6 (0.94×)	1,429.0 (0.95×)	1,951.8 (1.30×)
HumanEval	1,546.5	1,296.8 (0.84×)	1,330.1 (0.86×)	1,799.0 (1.16×)
MBPP	1,507.7	1,314.9 (0.87×)	1,361.3 (0.90×)	1,886.8 (1.25×)
MT-Bench	1,507.4	1,159.7 (0.77×)	1,115.5 (0.74×)	1,525.3 (1.01×)

Citation

If you find DFlash 2 useful, please cite:

@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}

Please also cite the original DFlash paper:

@inproceedings{chen2026dflash,
  title     = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author    = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}

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DFlash 2

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GGUF DFlash 2 draft models with parallel block drafting and candidate path selection. • 4 items • Updated about 16 hours ago