alt.freeweights.text / XYZ-Aquila-mini
From: XYZAILab <huggingface.co/XYZAILab>Newsgroups: alt.freeweights.textSubject:

XYZ-Aquila-mini

Date: 2026-11-15Commit: c70d151Signed: 2DC28CC7B26A283B ✓

Text generation model with 35.1B parameters. Safetensors weights, 70.2 GB in all. Published by XYZAILab under Apache-2.0.

35.1B params70.2GBXYZ-Aquila-minisafetensorspermissive
FactsParams35.1BSize70.2 GBFormatssafetensorsTasktext generationLicenseApache-2.0From sourcepermissive
$ fw get XYZAILab/XYZ-Aquila-mini
# manifest sha256 f49424eed063… · 70.2 GB · 26 files
$ fw verify ./XYZ-Aquila-mini XYZAILab/XYZ-Aquila-mini
From sourceHosted (signed)XYZAILab/XYZ-Aquila-miniLast check: 2026-11-15 03:53 UTC

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

Fine-tune of Qwen3.6-35B-A3B.

Works with

Overview

Size
70.2 GB
Weights
70.2 GB (safetensors)
Parameters
35.1B
Formats
safetensors
Task
text generation
License
Apache-2.0 (permissive)
Files
26, each with a sha256
Publisher
XYZAILab
Added
2026-11-15
Section
alt.freeweights.text
Lineage

Original release from the publisher.

Base model: Qwen/Qwen3.6-35B-A3B

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Model card

From the publisher at commit c70d151. Shown as plain text with links; images open at the publisher.

XYZ AI Lab — We Build The Minds That Build

XYZ-Aquila-mini#

An open-weight thinking model for Deep Search.

Homepage Demo AI4AI Demo Search Agent

GitHub Data Technical Report

Introduction#

XYZ-Aquila is a family of open-weight Deep Search agents developed by XYZ AI Lab. XYZ-Aquila-mini is post-trained from Qwen3.6-35B-A3B through a bounded-exploration AI4AI pipeline: humans define the target capability, development evidence, constraints, risk boundaries, and acceptance policy, while AI agents diagnose failures and propose scoped interventions across data, post-training, runtime, context management, tools, evaluation, and infrastructure.

The released checkpoint is a thinking model with Qwen-compatible reasoning and tool-call formats. It is optimized for agentic search, including long-horizon planning, English and Chinese web browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions. The open-source AxisAgentic harness provides the concrete search / scrape / python tool implementations, fixed tool contract, replayable context management, and benchmark evaluation workflow; these capabilities are supplied by the surrounding harness rather than by the checkpoint alone.

New Release#

Benchmark Results#

The external benchmark suite was held out from routine AI4AI optimization. Following the technical report, evaluation uses a ReAct-style search harness with web search, webpage extraction, stateful Python execution, and a maximum 256K context. XYZ-Aquila-mini obtains the highest reported score in every column of the evaluated sub-40B open-weight comparison. XYZ-Aquila-pro obtains the highest reported score in every column of the evaluated sub-400B open-weight comparison.

Image: XYZ-Aquila benchmark results across six agentic search benchmarks

The figure provides a visual overview across six agentic search benchmarks. The tables below transpose the comparison: each row is a benchmark and each column is a model within the group.

Small-scale open-weight (<40B)#

Open-weight systems with fewer than 40B parameters, including XYZ-Aquila-mini.

BenchmarkXYZ-Aquila-miniAgents-A1Nex-N2-miniapodex-miniMiroThinker 1.7 mini
BrowseComp78.875.574.171.567.9
BrowseComp-ZH82.9--79.6†80.6--
DeepSearchQA89.5--87.2†82.2--
GAIA97.196.0----80.3
LiveBrowseComp48.729.6†41.4†32.8†34.9†
HLE51.147.637.1†46.836.4
WideSearch80.8--62.0--73.3†
Large-scale open-weight (<400B)#

Open-weight systems with fewer than 400B parameters, including XYZ-Aquila-pro.

BenchmarkXYZ-Aquila-proNex-N2-ProMiroThinker 1.7apodex-1.0
BrowseComp84.883.7†74.075.5
BrowseComp-ZH85.179.6†75.382.6
DeepSearchQA92.592.3†--84.6
LiveBrowseComp53.750.4†34.1†--
HLE53.350.0†42.949.0
WideSearch81.275.6----
XYZ-Aquila-pro vs. Larger-Scale and Closed-Source Models#

XYZ-Aquila-pro compared with larger-scale open-weight and closed-source models.

| Benchmark | XYZ-Aquila-pro | apodex-h1 | DeepSeek-V4- Pro-Max | Kimi-K2.6 | Claude Opus 4.7 | GPT-5.5 xhigh |

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

| BrowseComp | 84.8 | 90.3 | 83.4 | 83.2 | 79.3 | 84.4 |

| BrowseComp-ZH | 85.1 | 84.1 | -- | -- | -- | -- |

| DeepSearchQA | 92.5 | 94.4 | -- | 92.5 | 89.1 | -- |

| LiveBrowseComp | 53.7 | -- | 38.3 | 31.7 | -- | -- |

| HLE | 53.3 | 60.8 | -- | 55.5 | 54.7 | 52.2 |

| WideSearch | 81.2 | -- | -- | 80.8 | -- | -- |

All values are percentages. HLE denotes Humanity's Last Exam. DeepSearchQA uses F1, WideSearch uses Item F1 Max@4, and the remaining benchmarks use accuracy. Rows with no reported result across an entire group are omitted; -- indicates an unreported result within an otherwise populated row. † marks results reproduced under the common evaluation setup; other baseline values come from public reports or benchmark submissions. See the technical report for full provenance and analysis.

Quickstart#

Install SGLang#

We recommend sglang>=0.5.14 for XYZ-Aquila-mini.

uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'
SGLang Deployment#

The example below launches an OpenAI-compatible endpoint with Qwen reasoning and tool-call parsers. It uses tensor parallelism across eight GPUs; reduce the tensor parallel size and context length if the deployment does not have enough memory.

MODEL_PATH=XYZAILab/XYZ-Aquila-mini
SERVED_MODEL=XYZ-Aquila-mini

python -m sglang.launch_server \
  --model-path "${MODEL_PATH}" \
  --served-model-name "${SERVED_MODEL}" \
  --port 8000 \
  --tp-size 8 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder
Tool-Use Example#

The following example sends a text-only research conversation with three tools to the SGLang OpenAI-compatible endpoint.

import requests

BASE_URL = "http://localhost:8000/v1"
MODEL_NAME = "XYZ-Aquila-mini"

SYSTEM_PROMPT = r"""You are a task-solving research agent with access to native structured tools.
Use the tools step-by-step to answer the user's question accurately and completely.
Today is: 2026-06-27

# Tool-Use Rules

- Use the function-calling interface to invoke tools. Do not write tool calls as plain text.
- Call at most ONE tool per assistant turn. The next message will contain that tool's result; use it to decide your next step.
- Only use tools declared in the provided tools schema.
- Use web_search to discover relevant sources, verify facts, or find promising URLs.
- Use scrape_and_extract_info to read a specific URL or extract focused facts from a page.
- Prefer focused searches and focused extraction requests. Avoid repeating the same query or URL unless the previous result was unusable.
- For source-grounded questions, do not rely only on search snippets when a page needs to be inspected.

# Answering

- When you have sufficient evidence, stop calling tools and provide the final answer wrapped in \boxed{}.
- Follow the user's requested output format exactly.
- Keep the boxed answer concise: a number, short phrase, or comma-separated list when appropriate.
- Do not output a final prose response without \boxed{}.
"""

USER_PROMPT = r"""In the mythological literature of a certain northern European region, the parental figure of a specified character was joined in a legal and social union with a ruler whose title was derived from a specific tribal designation. The name of that tribe was also adopted as the identifier for a historical Continental kingdom, whose final independent sovereign fell in armed conflict against a nomadic steppe confederation in the latter half of the 5th century. The character in question administered a prepared liquid to a male subject who had, in the narrative, taken possession of a hoard of precious metal and crafted objects. The hoard's origin was a confrontation between a male figure and a winged, venomous creature that had once been a male member of a specific lineage. The administration of the liquid led to the subject developing an altered perception of a prior commitment of matrimony. The later consequence, directly flowing from this event, was the coerced marriage of the subject's sister to the leader of the aforementioned nomadic confederation. Provide the name recorded for the character who prepared and administered the liquid.
Follow the request's format instructions strictly and wrap the final answer in \boxed{}."""

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": USER_PROMPT},
]

tools = [
    {
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "Search the web using Google and return relevant results. Use this tool to find up-to-date information, verify facts, or discover URLs for further investigation.\n\nReturns structured search results including organic results, knowledge graph, and related questions.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query string.",
                    },
                    "num": {
                        "type": "integer",
                        "description": "Number of results to return (default: 10, max: 100).",
                        "default": 10,
                    },
                    "hl": {
                        "type": "string",
                        "description": "Language code for results, e.g., 'en', 'zh', (ISO 639-1, default: 'en').",
                        "default": "en",
                    },
                    "gl": {
                        "type": "string",
                        "description": "Country code for localised results, e.g., 'us', 'gb', (ISO 3166-1 alpha-2, default: 'us').",
                        "default": "us",
                    },
                },
                "required": ["query"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "scrape_and_extract_info",
            "description": "Fetch and read the contents of a web page, PDF, or other online document. Returns the page content as clean text. When 'info_to_extract' is provided, uses an LLM to extract only the relevant information from the page.\n\nUse this tool to read web pages found via web_search, examine documents, or access any URL-addressable content.",
            "parameters": {
                "type": "object",
                "properties": {
                    "url": {
                        "type": "string",
                        "description": "The URL to scrape (web page, PDF, code file, etc.).",
                    },
                    "info_to_extract": {
                        "type": "string",
                        "description": "What specific information to extract from the page. If provided, returns a focused extraction instead of the full page content. If omitted, returns the full page content.",
                    },
                },
                "required": ["url"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "run_python_code",
            "description": "Execute a self-contained snippet of Python code in a fresh, isolated sandbox and return its output.\n\nEach call runs in a brand-new sandbox with no memory of previous calls: include every import and define every variable you need inside `code`, and `print(...)` anything you want to read back. Use it for calculations, symbolic math (sympy), numeric work (numpy/scipy), data processing, and verifying intermediate results.\n\nArgs:\n    code: The complete Python source to execute.\n\nReturns:\n    The captured execution result (stdout, returned values, and any error/traceback).",
            "parameters": {
                "type": "object",
                "properties": {
                    "code": {
                        "type": "string",
                        "description": "Self-contained Python code to execute. Print any value you want returned.",
                    },
                },
                "required": ["code"],
            },
        },
    },
]

payload = {
    "model": MODEL_NAME,
    "messages": messages,
    "tools": tools,
    "temperature": 1.0,
    "top_p": 0.95,
    "repetition_penalty": 1.05,
    "max_tokens": 32768,
    "chat_template_kwargs": {
        "enable_thinking": True,
        "preserve_thinking": True,
    },
}

response = requests.post(f"{BASE_URL}/chat/completions", json=payload, timeout=1800)
response.raise_for_status()
result = response.json()
temperature: 1.0
top_p: 0.95
repetition_penalty: 1.05
max_tokens: 32768
chat_template_kwargs:
  enable_thinking: true
  preserve_thinking: true

Citation#

@techreport{xyz_aquila_2026,
  title       = {AI4AI at Scale: A Full-Pipeline System for Enhancing LLM Agentic Capabilities},
  author      = {{XYZ Agentic Team}},
  institution = {XYZ AI Lab},
  year        = {2026},
  url         = {https://xyz-lab.ai/blogs/ai4ai-at-scale/assets/bounded-exploration-ai4ai-system-optimization.pdf}
}

Files

26 files · 70.2 GBWeights 70.2 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 26 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md15.0 KBce3eccff4e52012b0eb3c022685032358a214a7303f4193f37fb148a4dced417
assets/benchmark_results_candidate_2.svg229 KB75c5819cc8f1793ab084a80180f4323be8f4a0e7bca6489d1975657a068a4dee
assets/xyz-ai-lab-slogan.svg3.92 KBb484bb86c7ae27841071eae8a17c9e9468c343712f99275563cdc762ec17ba54
chat_template.jinja7.76 KBe84f32a23fdda27689f868aa4a1a5621f41133e51a48d7f3efcbea2839574259
config.json3.22 KB6c0f9a4e168600de8b916c07f5e53218a36df31e6fffc6713171d6019778929e
model-00001-of-00015.safetensors5.00 GBf25c551453c579b980daec36c4f13f4c2fc4c7e24555247bccd586325d1d9c12
model-00002-of-00015.safetensors5.00 GBd69b16612c47a69b53a6cd94739c7d8faa6f3b142bfe5a03e14523e45afe2be7
model-00003-of-00015.safetensors5.00 GB340145ec0d9a0bad1f35ca643264f4c03e64c7dfa72f8dd85483bd2638beff0a
model-00004-of-00015.safetensors5.00 GB6861c35899f1922a6bb0c50b29f1d919bf7cd567fa11172ed7def9d7e95d6b4d
model-00005-of-00015.safetensors5.00 GB205d7a73caf11a6d2a50857292b8a03db05961592f131bc8030cd386493096ab
model-00006-of-00015.safetensors5.00 GB1b4d399ebbe5d72876213f84e531c2ddf56e5112997c3cb0c595cfc7c493b41b
model-00007-of-00015.safetensors5.00 GB8f3accbde13310732b334307ecddffb0a70b8d73f0d5ce47594d706c2c6dfb01
model-00008-of-00015.safetensors5.00 GB1171cfa6f5286954ca6daa4a505ffdf80c7b5a8dec7b7a6294b3f819b9ccaaea
model-00009-of-00015.safetensors5.00 GB47ad9b333246a9ba3f538ef52aa29301d7435693bec4dc4065ff8d986f8fd278
model-00010-of-00015.safetensors5.00 GB7d7528f07929a5c4ebaa31de120d8327b24d4bbd9ee89a053c4240f31ffcb08a
model-00011-of-00015.safetensors5.00 GB43028fe145a9f5fccf65b06ad8bc042b0aeb895b160428298b961bda5ae17ebb
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model-00015-of-00015.safetensors1.02 GB71db7b9cdd684137f45884140bee8682d6fd4f452f20555a137acbee453c0d78
model.safetensors.index.json3.33 MB27cb9eb5b2b45faa3c298671f5a38c9db1e3431d949e14cf63c19a644abdc2ad
preprocessor_config.json390 B27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516
processor_config.json1.19 KBd89ef49ce9cd37fbf510158e13c1ef063d9286411c1ec9049932dbe0487143b1
tokenizer.json20.0 MB06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
tokenizer_config.json1.17 KB792fa3f0cb88b111e54ef3134c873531008c4df471d108da17903426e308aa7b
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
c70d151a71 pinned now2026-11-15 03:53 UTC26 files · 70.2 GB

Use it

$ fw get XYZAILab/XYZ-Aquila-mini
# manifest sha256 f49424eed063… · 70.2 GB · 26 files
$ fw verify ./XYZ-Aquila-mini XYZAILab/XYZ-Aquila-mini
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/XYZAILab/XYZ-Aquila-mini.json --dir ./XYZ-Aquila-mini $ python3 fw.py verify ./XYZ-Aquila-mini ./XYZAILab__XYZ-Aquila-mini.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download XYZAILab/XYZ-Aquila-mini --revision c70d151a710715886f013423d2a9ffbcba754858 --local-dir ./XYZ-Aquila-mini
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("XYZAILab/XYZ-Aquila-mini", revision="c70d151a710715886f013423d2a9ffbcba754858") model = AutoModelForCausalLM.from_pretrained("XYZAILab/XYZ-Aquila-mini", revision="c70d151a710715886f013423d2a9ffbcba754858", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve XYZAILab/XYZ-Aquila-mini --revision c70d151a710715886f013423d2a9ffbcba754858

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
README.md
Text sha256
ce3eccff4e52012b0eb3c022685032358a214a7303f4193f37fb148a4dced417
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: XYZAILab <huggingface.co/XYZAILab>Newsgroups: alt.freeweights.textSubject: XYZ-Aquila-miniDate: 2026-11-15Commit: c70d151Signed: 2DC28CC7B26A283B ✓
Upstream
XYZAILab/XYZ-Aquila-mini
Pinned commit
c70d151a710715886f013423d2a9ffbcba754858
Manifest
XYZ-Aquila-mini.json · .minisig
Manifest sha256
f49424eed06325918f541ce88693edae468eef32f69461ccff247fc325ee6038
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 03:53 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/XYZAILab/XYZ-Aquila-mini.json $ curl -O https://freeweights.allternit.com/manifests/XYZAILab/XYZ-Aquila-mini.json.minisig $ minisign -Vm XYZ-Aquila-mini.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

Model tree

Parent

Qwen/Qwen3.6-35B-A3B · this model is a finetune of it

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

Quantizations
1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download XYZAILab/XYZ-Aquila-mini
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. XYZ-Aquila-mini.json. Provenance. Takedown history: none.

Variants

1 quantized or adapter build of XYZ-Aquila-mini. Each has its own license check and page.

From: laion <huggingface.co/laion>Subject: a2-rl-e2egit_large-50-32B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-large-15-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-v2-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_pymethods2test-large-80-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_unitsyn-python-large-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_mix_h2_language_proportional-65-8B