XHToken/Spark-X2.6-4B-FP8

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Spark-X2.6-4B: Advancing the Frontier of On-Device Agents for Real-World Tasks

[blocked]

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

Introduction

A little over a month has passed since Spark-X2.5. In the rapidly evolving world of AI agents, that is enough time to rethink, refine, and move the frontier forward. Building on the Spark-X2.5-4B, we introduce Spark-X2.6-4B, the latest generation of our compact, general-purpose language models for on-device deployment.

Spark-X2.6-4B retains exactly the same architecture as Spark-X2.5-4B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These advances enable more capable and practical on-device AI for developers and users tackling real-world tasks.

Technical Highlights:

Stronger Coding, Agentic, and Long-Context Capabilities: The models deliver substantial improvements in agentic workflows involving complex tool use, multi-step planning, and software engineering. They achieve state-of-the-art performance among similarly sized open-source models, matching or even surpassing models with approximately 9B parameters.
Controllable Thinking Budget: The models provide three configurable thinking levels—none, low, high—allowing applications to balance reasoning quality, latency, and inference cost dynamically.

Benchmarks

We evaluate our models and compare them with leading on-device models of similar size across a broad range of tasks, including agent, code, math, general and knowledge. Spark-X2.6-4B achieves best-in-class performance among models of comparable size, while matching or exceeding the performance of larger 9B models on several benchmarks.

Edge Inference

Spark-X2.6-4B delivers inference efficiency on par with or better than comparable on-device models, including Qwen3.5-4B and Gemma-E4B. It combines this efficiency with stronger capabilities, making it a practical choice for responsive, resource-conscious on-device AI.

Quickstart

Transformers

Install the required version with pip install transformers==4.57.1.

python

from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "XHToken/Spark-X2.6-4B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(    model_id,    trust_remote_code=True,    torch_dtype="auto",    device_map="auto",)messages = [{"role": "user", "content": "What is the capital of Anhui Province?"}]inputs = tokenizer.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    enable_thinking=True,    return_dict=True,    return_tensors="pt",).to(model.device)outputs = model.generate(    **inputs,    max_new_tokens=131072,    do_sample=True,    temperature=1.0,    top_p=0.95,    top_k=0,)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

SGLang

The examples below serve a local Spark-X2.6-4B checkpoint. Set MODEL_PATH to its absolute path before starting a container:

Install SGLang

Use the pre-built image that tracks the Spark-X2.6 runtime:

For NVIDIA GPUs:

For Ascend NPUs:

bash

# A3 daily buildexport SGLANG_IMAGE=quay.io/ascend/sglang:main-cann9.0.0-a3
# A2 daily build (use this instead on A2 hardware)export SGLANG_IMAGE=quay.io/ascend/sglang:main-cann9.0.0-910b
docker pull "$SGLANG_IMAGE"

Run Inference

The following commands start an OpenAI-compatible API server configured for a maximum context length of 1,048,576 tokens. This setting requires sufficient device memory; reduce --context-length when necessary.

Server

NVIDIA GPU:

bash

docker run --rm -it \  --gpus '"device=0"' \  --ipc=host \  -p 30000:30000 \  -v "$MODEL_PATH:/root/Spark-X2.6-4B:ro" \  lmsysorg/sglang:nightly-dev-cu13-20260827-20621aa1 \  python -m sglang.launch_server \    --model-path /root/Spark-X2.6-4B \    --served-model-name spark2.6 \    --tool-call-parser spark25 \    --reasoning-parser qwen3 \    --tp-size 1 \    --mem-fraction-static 0.8 \    --context-length 1048576 \    --chat-template /root/Spark-X2.6-4B/chat_template.jinja \    --host 0.0.0.0 \    --port 30000

Ascend NPU:

bash

docker run -it --rm -e ASCEND_USE_FIA=1 --network=host --ipc=host --shm-size=16g \    --device=/dev/davinci0 --device=/dev/davinci1 --device=/dev/davinci2 --device=/dev/davinci3 \    --device=/dev/davinci4 --device=/dev/davinci5 --device=/dev/davinci6 --device=/dev/davinci7 \    --device=/dev/davinci8 --device=/dev/davinci9 --device=/dev/davinci10 --device=/dev/davinci11 \    --device=/dev/davinci12 --device=/dev/davinci13 --device=/dev/davinci14 --device=/dev/davinci15 \    --device=/dev/davinci_manager \    --device=/dev/devmm_svm \    --device=/dev/hisi_hdc \    --volume /usr/local/sbin:/usr/local/sbin \    --volume /usr/local/Ascend/driver:/usr/local/Ascend/driver \    --volume /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \    --volume /etc/ascend_install.info:/etc/ascend_install.info \    --volume /var/queue_schedule:/var/queue_schedule \    --volume ~/.cache/:/root/.cache/ \    --volume "$MODEL_PATH:/root/Spark-X2.6-4B:ro" \    --entrypoint=python \    "$SGLANG_IMAGE" \    -m sglang.launch_server \      --model-path /root/Spark-X2.6-4B \      --served-model-name spark2.6 \      --tool-call-parser spark25 \      --reasoning-parser qwen3 \      --tp-size 1 \      --mem-fraction-static 0.8 \      --context-length 1048576 \      --chat-template /root/Spark-X2.6-4B/chat_template.jinja \      --host 0.0.0.0 \      --port 30000

Client

Thinking is enabled by default by both the chat template and the Qwen3 reasoning parser. To disable thinking for a specific request, set "chat_template_kwargs": {"enable_thinking": false}.

bash

curl -s http://localhost:30000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "spark2.6",    "messages": [      {        "role": "user",        "content": "安徽的省会在哪里？"      }    ],    "max_tokens": 131072,    "temperature": 1,    "top_k": -1,    "top_p": 0.95,    "repetition_penalty": 1,    "presence_penalty": 0,    "frequency_penalty": 0  }'

vLLM

Deploy vLLM

vLLM provides an official Docker image for NVIDIA GPU deployment:

bash

docker run --rm --gpus all \  --ipc=host \  -p 30000:30000 \  -v "$MODEL_PATH:/models/Spark-X2.6-4B:ro" \  vllm/vllm-openai:latest \  --model /models/Spark-X2.6-4B \  --port 30000 \  --trust-remote-code \  --served-model-name spark25 \  --tensor-parallel-size 1 \  --gpu-memory-utilization 0.7 \  --enable-prefix-caching \  --chat-template /models/Spark-X2.6-4B/chat_template.jinja

For Ascend NPUs, choose an official image for the fastest setup.

Ascend A2:

bash

export IMAGE=quay.io/ascend/vllm-ascend:nightly-maindocker pull "$IMAGE"
export DEVICE=/dev/davinci0export MODEL_CACHE="${HOME}/.cache"
mkdir -p "$MODEL_CACHE"
docker run --rm \    --name vllm-ascend \    --shm-size=1g \    --device "$DEVICE" \    --device /dev/davinci_manager \    --device /dev/devmm_svm \    --device /dev/hisi_hdc \    -v /usr/local/dcmi:/usr/local/dcmi \    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \    -v /etc/ascend_install.info:/etc/ascend_install.info \    -v "$MODEL_CACHE:/root/.cache" \    -p 8000:8000 \    -it "$IMAGE" bash

Ascend A3:

bash

export IMAGE=quay.io/ascend/vllm-ascend:nightly-main-a3docker pull "$IMAGE"
export DEVICE0=/dev/davinci0export DEVICE1=/dev/davinci1export MODEL_CACHE="${HOME}/.cache"
mkdir -p "$MODEL_CACHE"
docker run --rm \    --name vllm-ascend \    --shm-size=1g \    --device "$DEVICE0" \    --device "$DEVICE1" \    --device /dev/davinci_manager \    --device /dev/devmm_svm \    --device /dev/hisi_hdc \    -v /usr/local/dcmi:/usr/local/dcmi \    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \    -v /etc/ascend_install.info:/etc/ascend_install.info \    -v "$MODEL_CACHE:/root/.cache" \    -p 8000:8000 \    -it "$IMAGE" bash

Ascend 950DT:

bash

export IMAGE=quay.io/ascend/vllm-ascend:nightly-main-a5docker pull "$IMAGE"
export MODEL_CACHE="${HOME}/.cache"
mkdir -p "$MODEL_CACHE"
docker run --rm \    --name vllm-ascend \    --net=host \    --shm-size=1g \    --device /dev/davinci0 \    --device /dev/davinci_manager \    --device /dev/devmm_svm \    --device /dev/hisi_hdc \    -v /usr/local/dcmi:/usr/local/dcmi \    -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \    -v /etc/ascend_install.info:/etc/ascend_install.info \    -v "$MODEL_CACHE:/root/.cache" \    -it "$IMAGE" bash

Install the Spark plugin inside the container:

Server

bash

vllm serve "/models/Spark-X2.6-4B" \ --port "30000" \ --trust-remote-code \ --served-model-name spark25 \ --tensor-parallel-size 1 \ --gpu-memory-utilization 0.7 \ --enable-prefix-caching \ --chat-template /models/Spark-X2.6-4B/chat_template.jinja

Client

bash

curl -s http://127.0.0.1:30000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "spark25",    "messages": [{"role": "user", "content": "What is the capital of Anhui Province?"}],    "temperature": 1.0,     "top_k": -1,    "top_p": 0.95  }'

MLX

Spark-MLX-LLM runs the original Spark-X2.6 Hugging Face checkpoints locally. It supports Apple silicon GPU, Linux CPU, and NVIDIA CUDA on Linux. No GGUF conversion is required.

Installation

bash

git clone https://github.com/XHToken/Spark-MLX-LLM.gitcd Spark-MLX-LLM
python3 -m venv .venvsource .venv/bin/activate
# Apple siliconpython -m pip install -e .# Linux CPUpython -m pip install -e '.[cpu]'# Linux with CUDA 12python -m pip install -e '.[cuda12]' # Linux with CUDA 13python -m pip install -e '.[cuda13]'

Run Inference

llama.cpp

[!NOTE] Native spark2_5 support requires llama.cpp b10828 or later.

Run the model in the CLI:

Launch the OpenAI-compatible API server:

Ollama

Download and install Ollama from ollama.com (v0.34.1 or later).
Run the model:

LM Studio

Download and install LM Studio from lmstudio.ai (0.4.0 or later).
Search for "Spark-X2.6" in LM Studio and download the model.
Load the model and start chatting.

Run with the lms cli

Unsloth

Download Unsloth Studio

[!NOTE] The latest Unsloth Studio supports native Spark-X2.6 GGUF inference.

Fine-Tuning

We recommend using Llama-Factory to fine-tune the model.

License

The Spark-X2.6 model series is licensed under the Apache 2.0 License [blocked].

Citation

If you find our work helpful, feel free to give us a cite.