alt.freeweights.text / Spark-X2.6-1.7B

From: XHToken <huggingface.co/XHToken>Newsgroups: alt.freeweights.textSubject:

Spark-X2.6-1.7B

Date: 2026-11-15Commit: 0e60633Signed: 2DC28CC7B26A283B ✓

Text generation model with 1.7B parameters. Safetensors weights, 3.43 GB in all. Published by XHToken under Apache-2.0.

1.7B params3.43GBSpark-X2.6-1.7Bsafetensorspermissive

FactsParams1.7BSize3.43 GBFormatssafetensorsTasktext generationLicenseApache-2.0From sourcepermissive

$ fw get XHToken/Spark-X2.6-1.7B
# manifest sha256 ff0edf5c52cb… · 3.43 GB · 20 files
$ fw verify ./Spark-X2.6-1.7B XHToken/Spark-X2.6-1.7B

From sourceHosted (signed)XHToken/Spark-X2.6-1.7BLast check: 2026-11-15 02:49 UTC

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Get help: custom fine-tune or private deploy. We review your setup before we quote.

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Overview Model card Files Use it License Provenance Model tree

Overview

Size: 3.43 GB
Weights: 3.42 GB (safetensors)
Parameters: 1.7B
Formats: safetensors
Task: text generation
License: Apache-2.0 (permissive)
Files: 20, each with a sha256
Publisher: XHToken
Added: 2026-11-15
Section: alt.freeweights.text

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Base model: XHToken/Spark-X2.6-1.7B-Base

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Model card

From the publisher at commit 0e60633. Shown as plain text with links; images open at the publisher.

Spark-X2.6-1.7B#

Slack Discord YouTube dev.to Bluesky X Zhihu WeChat

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

Introduction#

We introduce Spark-X2.6-1.7B, the second model in the Spark-X2.6 series, following the release of Spark-X2.6-4B. Building on Spark-X2.5-1.7B, this release further advances our compact, general-purpose language models for on-device deployment.

Spark-X2.6-1.7B retains exactly the same architecture as Spark-X2.5-1.7B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These capability gains, combined with strong inference efficiency among models of comparable size, 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 model delivers substantial improvements in complex tool use, multi-step planning, software engineering, and reasoning over extended contexts, achieving state-of-the-art performance among open-source models of comparable size.
Efficient On-Device Inference: The model delivers strong on-device inference efficiency, achieving leading decoding throughput among similarly sized open-source models in our evaluations and enabling more responsive AI experiences on local hardware.
Controllable Thinking Budget: The model provides 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-X2.6-1.7B surpasses models of comparable size on most benchmarks, delivering state-of-the-art results.

Image: all Spark-X2.6 benchmarks

Edge Inference#

Spark-X2.6-1.7B delivers leading decoding throughput at shorter context lengths among the on-device models we evaluated, including Qwen3.5-2B, MiniCPM-2B, and LFM2.5-2.6B. Moreover, our optimized llama.cpp implementation(https://github.com/XHToken/llama.cpp) amplifies this performance advantage across all tested context lengths on the RTX 4090, underscoring its strong efficiency for responsive local AI.

Image: Spark-X2.6 inference

Reasoning Effort Comparison#

We compare high and low reasoning effort across agent, code, math, long-context, and general knowledge tasks. When switching from high to low, Spark-X2.6-1.7B average score decreases by 4.0 points, while its average output length drops by 37.3%. For Spark-X2.6-4B, the average score decreases by 1.7 points, while average output length drops by 22.0%. This flexibility allows developers to balance task performance and generation efficiency according to their application needs.

Image: Spark-X2.6 reasoning_effort

Quickstart#

Transformers#

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

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "XHToken/Spark-X2.6-1.7B"
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-X2.6-1.7B checkpoint. Set MODEL_PATH to its absolute path before starting a container:

export MODEL_PATH=/absolute/path/to/Spark-X2.6-1.7B

Install SGLang#

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

For NVIDIA GPUs:#

docker pull lmsysorg/sglang:nightly-dev-cu13-20260827-20621aa1

For Ascend NPUs:#

# A3 daily build
export 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:#

docker run --rm -it \
  --gpus '"device=0"' \
  --ipc=host \
  -p 30000:30000 \
  -v "$MODEL_PATH:/root/Spark-X2.6-1.7B:ro" \
  lmsysorg/sglang:nightly-dev-cu13-20260827-20621aa1 \
  python -m sglang.launch_server \
    --model-path /root/Spark-X2.6-1.7B \
    --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-X2.6-1.7B/chat_template.jinja \
    --host 0.0.0.0 \
    --port 30000

Ascend NPU:#

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 "$MODEL_PATH:/root/Spark-X2.6-1.7B:ro" \
    --entrypoint=python \
    "$SGLANG_IMAGE" \
    -m sglang.launch_server \
      --model-path /root/Spark-X2.6-1.7B \
      --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-X2.6-1.7B/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, default reasoning_effort set to "high". You can configure thinking behavior for each request through chat_template_kwargs: set reasoning_effort to "low" or "high", such as "chat_template_kwargs": {"reasoning_effort": "low"}, or set "chat_template_kwargs": {"enable_thinking": false} to disable thinking.

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？"
      }
    ],
    "chat_template_kwargs": {"reasoning_effort": "low"},
    "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:

docker run --rm --gpus all \
  --ipc=host \
  -p 30000:30000 \
  -v "$MODEL_PATH:/models/Spark-X2.6-1.7B:ro" \
  vllm/vllm-openai:latest \
  --model /models/Spark-X2.6-1.7B \
  --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-X2.6-1.7B/chat_template.jinja

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

Ascend A2:#

export IMAGE=quay.io/ascend/vllm-ascend:nightly-main
docker pull "$IMAGE"

export DEVICE=/dev/davinci0
export 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:#

export IMAGE=quay.io/ascend/vllm-ascend:nightly-main-a3
docker pull "$IMAGE"

export DEVICE0=/dev/davinci0
export DEVICE1=/dev/davinci1
export 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:#

export IMAGE=quay.io/ascend/vllm-ascend:nightly-main-a5
docker 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:

pip install uv
uv venv ~/spark2_6
source ~/spark2_6/bin/activate
git clone https://github.com/XHToken/Spark-plugin.git
cd ./Spark-plugin
uv pip install .

Server#

vllm serve "/models/Spark-X2.6-1.7B" \
 --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-X2.6-1.7B/chat_template.jinja

Client#

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#

git clone https://github.com/XHToken/Spark-MLX-LLM.git
cd Spark-MLX-LLM

python3 -m venv .venv
source .venv/bin/activate

# Apple silicon
python -m pip install -e .
# Linux CPU
python -m pip install -e '.[cpu]'
# Linux with CUDA 12
python -m pip install -e '.[cuda12]' 
# Linux with CUDA 13
python -m pip install -e '.[cuda13]'

Run Inference#

spark-mlx-generate \
  --device gpu \
  --dtype bfloat16 \
  --model XHToken/Spark-X2.6-1.7B \
  --prompt "What is the capital of Anhui Province?" \
  --max-tokens 512 \
  --temp 0

llama.cpp#

Note. Native spark2_5 support requires llama.cpp b10828 or later.

Run the model in the CLI:

llama cli -m /path/to/Spark-X2.6-1.7B-GGUF

Launch the OpenAI-compatible API server:

llama serve -m /path/to/Spark-X2.6-1.7B-GGUF

Ollama#

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

ollama run SparkLLM/Spark-X2.6-1.7B

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#

# List available models
lms ls

# Run chat with Spark-X2.6
lms chat spark-x2.6

Unsloth#

Download Unsloth Studio

Note. The latest Unsloth Studio supports native Spark-X2.6 GGUF inference.

WebGPU (Community)#

Run Spark-X2.6-1.7B locally in your browser with the community WebGPU demo contributed by Mohd Mahmodi.

Open the WebGPU demo in a WebGPU-compatible browser.
Download the model through the demo and start chatting. Inference runs on your local GPU.

ONNX model files · Source code and validation

Note. This community implementation uses the official Spark-X2.6-1.7B-INT4 weights and requires no local installation. Tested by the contributor on Windows with an NVIDIA GPU.

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.

Citation#

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

@misc{sparkx2.6,
    title  = {Spark-X2.6-4B&1.7B: Advancing the Frontier of On-Device Agents for Real-World Tasks},
    author = {SparkLLM Team},
    year   = {2026}
}

Files

20 files · 3.43 GBWeights 3.42 GB (safetensors). Sizes and sha256 from the signed manifest.

Hide all 20 files with their sha256

File	Size	sha256
.gitattributes	1.79 KB	b741b8835b5146e8495c608bbbd6de82e8d1d32ca0a1a12f646fee809097b88e
LICENSE	11.3 KB	525c465ab952779d2d360a9ff36f7f49ae018a23d80302ff839a850a77784c36
README.md	15.9 KB	a3853acb9766e2b88ad9725284aef81ca4eb670cd6056f69bca7608bc0a950c3
chat_template.jinja	5.72 KB	480576bd2ef80726a0dba6406d72ca37f4f4f9ca747c7f1c8d159dbdf40f6929
config.json	1.84 KB	16aa5d3619d0d893e7157f2499675430844eb7b383c80ecc1b3e8e8928a75d52
configuration_spark.py	4.33 KB	02218597240490f490659b184052db940b08163ff9c3af7b7e59323f6f963722
generation_config.json	281 B	804a5276a9d237600688267dd2bf6f9d4129ec21faf417f870d5d52b32ad9e6c
images/all_benchmark.svg	56.8 KB	653c626922bd6eeb24ca2b4be55739252635be9c77ad563850fe8103202af359
images/inference.svg	46.6 KB	d9867c5824e22af6ea9a15fb530f84b6120185144f8aecf46c4841d270435338
images/reasoning_effort.svg	13.6 KB	8d0b3f424a0cedc1703b9911ee715e19a1aaad468a686d68ad4ebfa13a97d5fa
images/xhtoken-wechat.jpg	160 KB	0c3fbd25c987237b383461fdaa96bf6881af615c23ba3baf19e51a7879354ddb
merges.txt	1.57 MB	b06fc88940ae6a20f71142f06304c12547f58ab52c175e56af12a38c3dc62f36
model-00001-of-00002.safetensors	2.00 GB	4461aaca4cac9b394ed951697e95a6da3c8952ed5976bbc162c5b085bdc0924a
model-00002-of-00002.safetensors	1.42 GB	aea734125f5ac9c460847b33836977fe6afa68861b2d687f82bfe7d2b544d699
model.safetensors.index.json	18.8 KB	cc2b212985f5d0469bf926903e4bd7ee81856687baa0c6c6b55ff200d4cdc63f
modeling_spark.py	19.4 KB	9cf0d1ad2b54b9f7088792779ddd8b5cb4d4fe63bfea054da7f2dd16362bcaf4
special_tokens_map.json	156 B	48a257e3f5f20a004e7c30482a70ae19ae7d9cf713144f2a1394ba20af8c3cb5
tokenizer.json	10.1 MB	710cce15cf3565674c499f9413997c6e8101f2bdd96245cff8f0311fb501248c
tokenizer_config.json	5.84 KB	57b385dcc2ad031aef4a3786e97cd9a832ecb839cb27ed2cd829d627c90e1bfd
vocab.json	3.03 MB	9f42cb7e7af5afe27cae2337d272d60b5659e10143ef4bb952f642b847916fe1

VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.

Upstream commit	Date	Contents
0e60633b34 pinned now	2026-11-15 02:49 UTC	20 files · 3.43 GB

Use it

$ fw get XHToken/Spark-X2.6-1.7B
# manifest sha256 ff0edf5c52cb… · 3.43 GB · 20 files
$ fw verify ./Spark-X2.6-1.7B XHToken/Spark-X2.6-1.7B

fw.pyChecks the signature and every sha256, tries each source in order, resumes.

$ python3 fw.py get https://freeweights.allternit.com/manifests/XHToken/Spark-X2.6-1.7B.json --dir ./Spark-X2.6-1.7B
$ python3 fw.py verify ./Spark-X2.6-1.7B ./XHToken__Spark-X2.6-1.7B.json

hfThe Hugging Face CLI with one setting. Same repo id.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download XHToken/Spark-X2.6-1.7B --revision 0e60633b345b19ba9f56ed1fda197acd271e8546 --local-dir ./Spark-X2.6-1.7B

transformersLoad the weights in Python.

import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("XHToken/Spark-X2.6-1.7B", revision="0e60633b345b19ba9f56ed1fda197acd271e8546")
model = AutoModelForCausalLM.from_pretrained("XHToken/Spark-X2.6-1.7B", revision="0e60633b345b19ba9f56ed1fda197acd271e8546", device_map="auto")

vLLMServe an OpenAI-compatible API.

$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve XHToken/Spark-X2.6-1.7B --revision 0e60633b345b19ba9f56ed1fda197acd271e8546

Run at home

License

Name: Apache License 2.0
Id: Apache-2.0
Class: Permissive
Redistribution: yes
Listing: Hosted (signed)
Decided: License as declared by the publisher. No person read it. 2026-11-15
Text in: LICENSE
Text sha256: 525c465ab952779d2d360a9ff36f7f49ae018a23d80302ff839a850a77784c36
Terms gate: No extra terms beyond the license.

Read the license text

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Provenance

From: XHToken <huggingface.co/XHToken>Newsgroups: alt.freeweights.textSubject: Spark-X2.6-1.7BDate: 2026-11-15Commit: 0e60633Signed: 2DC28CC7B26A283B ✓

Upstream: XHToken/Spark-X2.6-1.7B
Pinned commit: 0e60633b345b19ba9f56ed1fda197acd271e8546
Manifest: Spark-X2.6-1.7B.json · .minisig
Manifest sha256: ff0edf5c52cbd07d47945c77537b64997f5193c4fe427aabbf0742927d5071d5
Signature: minisign, Ed25519, key 2DC28CC7B26A283B
Created: 2026-11-15 02:49 UTC
Generator: freeweights-manifest/0.1.0
Takedowns: None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/XHToken/Spark-X2.6-1.7B.json
$ curl -O https://freeweights.allternit.com/manifests/XHToken/Spark-X2.6-1.7B.json.minisig
$ minisign -Vm Spark-X2.6-1.7B.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq
$ shasum -a 256 <file>   # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

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Parent

XHToken/Spark-X2.6-1.7B-Base · this model is a finetune of it

1 model in the catalog name this one as their parent.

Quantizations: 1

Quantizations 1

bartowski/Spark-X2.6-1.7B-GGUF26.1 GB

Get it

Download

$ HF_ENDPOINT=https://freeweights.allternit.com hf download XHToken/Spark-X2.6-1.7B

Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest

minisign, key 2DC28CC7B26A283B. Spark-X2.6-1.7B.json. Provenance. Takedown history: none.

Source

huggingface.co/XHToken/Spark-X2.6-1.7B

Variants

1 quantized or adapter build of Spark-X2.6-1.7B. Each has its own license check and page.

SubjectSizeParamsLicenseDate

Spark-X2.6-1.7B-GGUFHosted (signed) From: bartowski in alt.freeweights.text26.1 GB—Permissive2026-11-15

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Spark-X2.6-1.7B

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Overview

Model card

Spark-X2.6-1.7B#

Introduction#

Benchmarks#

Edge Inference#

Reasoning Effort Comparison#

Quickstart#

Transformers#

SGLang#

Install SGLang#

For NVIDIA GPUs:#

For Ascend NPUs:#

Run Inference#

Server#

NVIDIA GPU:#

Ascend NPU:#

Client#

vLLM#

Deploy vLLM#

Ascend A2:#

Ascend A3:#

Ascend 950DT:#

Server#

Client#

MLX#

Installation#

Run Inference#

llama.cpp#

Ollama#

LM Studio#

Run with the lms cli#

Unsloth#

WebGPU (Community)#

Fine-Tuning#

License#

Citation#

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