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alt.freeweights.text / Inkling-Small
From: thinkingmachines <huggingface.co/thinkingmachines>Newsgroups: alt.freeweights.textSubject:

Inkling-Small

Date: 2026-11-15Commit: 8cc5877Signed: 2DC28CC7B26A283B ✓

Image text to text model with 266B parameters. Safetensors weights, 532 GB in all. Published by thinkingmachines under Apache-2.0.

266B params532GBInkling-Smallsafetensorspermissive
FactsParams266BSize532 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get thinkingmachines/Inkling-Small
# manifest sha256 09ac01838f1b… · 532 GB · 50 files
$ fw verify ./Inkling-Small thinkingmachines/Inkling-Small
From sourceHosted (signed)thinkingmachines/Inkling-SmallLast check: 2026-11-15 02:26 UTC
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Overview

Size
532 GB
Weights
532 GB (safetensors)
Parameters
266B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
50, each with a sha256
Publisher
thinkingmachines
Added
2026-11-15
Section
alt.freeweights.text
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Original release from the publisher.

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Model card

From the publisher at commit 8cc5877. Shown as plain text with links; images open at the publisher.

Inkling#

Image: image

BF16 | NVFP4 | Playground | Tinker Cookbook | Acceptable Use

1. General Information#

Inkling-Small is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.

Languages: English, with general multilingual capabilities across other languages.

2. Getting Started#

Try Inkling-Small on the Tinker Playground or access via API using the Tinker Cookbook.

Inkling-Small supports local deployment using the following open-source libraries:

API access is also available through third party inference providers.

3. Model Properties#

Model type#

Multimodal autoregressive transformer

Architecture type#

A 42-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) feed-forward backbone: each token is routed to 6 of 256 experts, plus 2 shared experts active on every token. Attention is a hybrid of local and global layers. The model is natively multimodal — images are encoded via a hierarchical patch encoder, and audio via discrete token encoding — with all modalities projected into a shared hidden space and processed jointly by the decoder.

Parameters#

276B total, 12B active

Numerics support#

BF16 and NVFP4

Input modalities#

Inkling-Small accepts text, image, and audio inputs:

  • Text: UTF-8 encoded text
  • Image: Any pixel-based image input. For optimal performance, each image dimension should be between 40px to 4096px.
  • Audio: WAV format, sampled at 16kHz. For optimal performance, audio length should ideally be under 2 mins.
Output modalities#

Inkling-Small generates output as UTF-8 encoded text.

4. Training#

Training data includes a broad variety of content types, including text, images, audio, video.

Training data for the model was drawn from publicly available sources, acquired from third-parties, or synthetically generated or augmented. Publicly available data includes content from the public internet and publicly accessible repositories.

The training data curation process includes cleaning, processing, and modifying datasets. These processing steps, which vary by data type, may include deduplication and filtering to remove junk or other low-quality data, or to advance safety or other objectives.

5. Evaluations#

| | Open weights

| Closed weights

| | Inkling-Small | Qwen3.5 397B-A17B | MiMo V2.5 | Minimax M2.7 | DeepSeek V4 Flash | Nemotron 3 Ultra | Inkling | Claude 4.5 Haiku | Gemini 3.5 Flash-Lite | GPT 5.6 Luna

| Model Info | | | | | | | | | |

| AA Index (v4.1)

| 40.0% | 34.0% | 37.0% | 38.0% | 40.0% | 38.0% | 41.0% | 30.0% | 36.0% | 49.0%

| Params (B) (activated / total)

| 12 / 276 | 17 / 397 | 15 / 310 | 10 / 230 | 13 / 284 | 55 / 550 | 41 / 975 | – | – | –

| Agentic (coding) | | | | | | | | | |

| SWEBench Verified

| 80.2% | 76.4% | 71.0% | 79.9% | 79.0% | 70.7% | 77.6% | 73.3% | 75.0% | 93.0%

| SWEBench Pro (public)

| 55.9% | 50.9% | 56.1% | 56.2% | 52.6% | 46.4% | 54.3% | 39.5% | 54.2% | 62.7%

| Terminal Bench 2.1 (best harness)

| 64.7% | 51.3% | 63.7% | 55.4% | 61.8% | 56.4% | 63.8% | 44.2% | 54.0% | 82.5%

| SciCode

| 48.7% | 42.0% | 43.1% | 47.0% | 44.9% | 39.9% | 46.1% | 43.3% | 40.9% | 50.0%

| Agentic (general) | | | | | | | | | |

| GDPval-AA v2

| 1269 | 962 | 1145 | 1159 | 1189 | 1164 | 1238 | 911 | 1139 | 1530

| MCP Atlas (public / all)

| 79.6/79.2% | 74.2%/– | – | 49.4%/– | 69.0%/– | 47.4/44.7% | 78.8/76.0% | 41.2/40.2% | 79.8/76.8% | 77.0/75.0%

| Tau 3 Banking

| 15.5% | 13.4% | 6.6% | 8.9% | 22.9% | 13.8% | 23.7% | 9.1% | 16.5% | 24.3%

| BrowseComp (with context management)

| 77.4% | 78.6% | – | 76.3% | 73.2% | 63.0% | 77.1% | – | – | 84.0%

| Toolathlon Verified

| 54.4% | 40.7% | 49.1% | 47.5% | 50.9% | 34.3% | 45.5% | 26.9% | 57.1% | 67.9%

| AA-Briefcase

| 917 | – | – | – | 833 | 870 | 839 | 612 | – | –

| Reasoning (general) | | | | | | | | | |

| GPQA Diamond

| 89.5% | 89.3% | 84.9% | 87.4% | 89.4% | 86.7% | 87.2% | 67.2% | 83.8% | 89.5%

| HLE (text only)

| 31.6% | 27.3% | 25.2% | 28.1% | 32.1% | 26.6% | 29.7% | 9.7% | 17.5% | 35.6%

| HLE (with tools)

| 47.8% | 48.3% | 40.0% | 40.3% | 45.1% | 37.4% | 46.0% | 17.8% | 42.5% | 48.9%

| AIME 2026

| 95.5% | 93.3% | 93.6% | 87.7% | 95.8% | 94.2% | 97.1% | 85.1% | 82.2% | 97.6%

| HMMT Feb 2026

| 90.2% | 87.9% | 82.6% | 71.2% | 93.9% | 78.8% | 86.3% | 66.7% | 63.6% | 98.5%

| CritPt

| 8.3% | 1.7% | 3.7% | 0.6% | 7.1% | 3.1% | 5.4% | 0.0% | 0.0% | 20.6%

| Reasoning (abstract) | | | | | | | | | |

| ARC-AGI-1

| 84.0% | – | – | – | – | – | 79.5% | 47.7% | – | 87.7%

| ARC-AGI-2

| 40.1% | – | – | – | – | – | 36.5% | 4.0% | – | 47.6%

| Factuality | | | | | | | | | |

| SimpleQA Verified

| 20.6% | 26.0% | 16.1% | 13.5% | 34.1% | 32.4% | 43.9% | 5.9% | 44.1% | 41.7%

| AA Omniscience (index)

| -9.0 | -29.8 | -9.3 | 0.7 | -22.9 | -1.0 | 2.1 | -4.2 | 6.9 | -11.6

| Chat | | | | | | | | | |

| IFBench

| 82.2% | 78.8% | 67.1% | 75.7% | 79.2% | 81.4% | 79.8% | 54.3% | 78.6% | 67.3%

| Global-MMLU-Lite

| 86.7% | 90.0% | 83.5% | 83.9% | 88.4% | 85.6% | 88.7% | 83.4% | 89.4% | 88.7%

| Safety | | | | | | | | | |

| StrongREJECT

| 98.4% | 99.4% | 99.3% | 99.4% | 97.4% | 98.7% | 98.6% | 98.6% | 97.6% | 98.7%

| FORTRESS (adversarial)

| 71.6% | 77.3% | 64.8% | 86.3% | 32.0% | 77.6% | 78.0% | 91.3% | 70.7% | 83.8%

| FORTRESS (benign)

| 96.9% | 95.4% | 94.6% | 90.1% | 99.2% | 90.6% | 95.9% | 94.1% | 95.5% | 97.8%

| Vision | | | | | | | | | |

| MMMU Pro (Standard 10)

| 74.0% | 77.3% | 75.4% | – | – | – | 73.5% | 58.6% | 79.0% | 78.6%

| Charxiv RQ (original / with python)

| 77.4/81.3% | 80.8%/– | 81.0%/– | – | – | – | 78.1/82.0% | 57.4%/– | 70.0%/– | 81.4%/–

| Audio | | | | | | | | | |

| Audio MC

| 54.9% | – | 30.4% | – | – | – | 56.6% | – | 33.6% | –

| MMAU

| 77.0% | – | 73.6% | – | – | – | 77.2% | – | 75.2% | –

| VoiceBench

| 90.1% | – | 86.4% | – | – | – | 91.4% | – | 85.9% | –

  • Inkling-Small against open-and closed-weights models across the full eval suite. Activated and total parameters are given for scale; a dash means the score was not available at the time of writing.
  • SWEBench Verified: Inkling and Inkling-Small’s numbers are reported using a bash-only harness. We use self-reported numbers for external models.
  • Terminal Bench 2.1: Inkling and Inkling-Small’s numbers are reported using an internal coding harness. A small number of solutions were found to be contaminated from web search and were assigned a score of 0. We use self-reported numbers for external models where available. Otherwise, we report performance using our internal harness.
  • Audio MC: Other models were evaluated internally since they are not on the official leaderboard.
  • VoiceBench: VoiceBench uses rule-based, hard-coded string matching for grading, making the evaluation sensitive to output-formatting differences. We therefore added a system message instructing models to follow the expected answer format.
  • HLE with tools: We benchmarked Minimax M2.7, Claude 4.5 Haiku, Gemini 3.5 Flash-Lite, and GPT 5.6 Luna using our internal harness.

6. Safety#

We conducted safety evaluations ahead of release, spanning both everyday human-AI interaction and dangerous-capability testing. Because Inkling-Small is multimodal, we paid attention to whether safety behavior held consistently across text, audio, and image inputs. We applied mitigations to reduce risks before release.

For everyday interaction, we evaluated sycophancy, harmful manipulation, and psychological-harm patterns like parasocial dependency and validation of delusional reasoning, including through multi-turn, open-ended external red-teaming designed to surface issues that only emerge over longer conversations. We also assessed whether the model refuses genuinely harmful requests without over-refusing benign ones. For CBRN and cyber, we assessed knowledge and procedural uplift through internal evaluations, external testing, and refusal-suppressed variants intended to estimate latent capability with safeguards removed. For loss of control, we evaluated agentic capability, strategic deception, and sabotage potential, benchmarked against public frontier models, and found the model materially below frontier capabilities.

Across all areas, we concluded that Inkling-Small did not present risk of material uplift beyond what's already available in the open-weight ecosystem.

The residual risks identified in our evaluations — specifically, Inkling-Small’s occasional tendency to comply with role-play and indirectly framed prompts concerning harmful topics — are consistent with what you would see from any open-weight model, and are best addressed with defense-in-depth rather than relying on the model's refusals alone. Common downstream moderation tools, such as Llama Guard, are compatible with Inkling-Small and can be layered around the model to catch jailbreak attempts, filter unsafe outputs, and enforce use-case-specific policies. We would encourage treating this kind of input/output classification as a part of your deployment stack, especially for consumer-facing or high-traffic applications where adversarial prompting is more likely.

7. Bias, risks and limitations#

Inkling-Small may exhibit general limitations common to foundation models, including hallucination (generating plausible but factually incorrect or unsupported content), occasional failures to follow instructions precisely, and degraded performance in long multi-turn conversations. As with other large-scale models trained on web-derived and synthetic data, Inkling-Small may reflect biases present in its training data, including demographic, cultural, or linguistic biases, and may perform unevenly across languages, dialects, or subject domains that were less represented during training.

Inkling-Small's knowledge is limited to information available as of its training cutoff, and it may not reflect events, developments, or changes that occurred afterward.

We recommend that downstream developers and deployers apply appropriate human oversight and review for outputs used in high-stakes or safety-critical contexts, rather than relying on Inkling-Small's outputs without verification.

  • Conduct their own evaluation of Inkling-Small's performance, safety, and fairness for their specific use case, language, and population prior to deployment, particularly for applications involving vulnerable groups.
  • Implement additional safeguards – such as content filtering, rate limiting, and monitoring – at the application layer, especially for open deployment contexts where Inkling-Small's built-in mitigations may not be sufficient on their own.
  • Avoid deploying Inkling-Small in domains such as medical, legal, or safety-critical decision-making without additional fine-tuning, domain-specific validation, and human oversight

Training Data Documentation

Files

50 files · 532 GBWeights 532 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 50 files with their sha256
FileSizesha256
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.eval_results/gpqa.yaml164 Ba9634472f4a5908b8c8895d2f86177228c793a439f18b60f39d02dc639e0cdf6
.eval_results/hle.yaml172 Bcdc5b423e5b5d46cc796abe44261cb4c034bbbd50246eb7d9a6c15939e776075
.eval_results/hmmt_feb_2026.yaml188 Bbbbd4737ee94252435a8df80bad32077bebc39bcb36b243323ee793b6d84e766
.eval_results/mmmu_pro.yaml181 B41452190185b743de6600dc4a48fa6b6a5c5d6f9d3025e6b6cabbbcf11124401
.eval_results/swe-bench_pro.yaml176 B4939efc79bc4481c324f30647f086b9b02833c54ea28b2dd28fb03ed336993bb
.eval_results/swe-bench_verified.yaml190 B121788944a55a8f717a79f2113cf8d5d6a5d8f4791dabdb580e0e8419fb98a49
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processor_config.json1.11 KBb4a3962ea5f7ec39f40b5cf14e57ce99776c3dcce4756a110f7a169809e3a04c
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tiktoken/tokenizer.model3.62 MBbc253fd2b702f7a6da7105eaa8f3463b2f1247e83614f23e5323b921088bed2a
tokenizer.json27.9 MB9fb6333a7db8fe5da90728e741e4a3ee4ac2ae12c5dd4958cc6f31688787d3c2
tokenizer_config.json12.1 KB2e36c9748a2081abb935b2e745ee22e82efa32589c2500df7e5bc0f93145cd77
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
8cc5877b44 pinned now2026-11-15 02:26 UTC50 files · 532 GB

Use it

$ fw get thinkingmachines/Inkling-Small
# manifest sha256 09ac01838f1b… · 532 GB · 50 files
$ fw verify ./Inkling-Small thinkingmachines/Inkling-Small
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/thinkingmachines/Inkling-Small.json --dir ./Inkling-Small $ python3 fw.py verify ./Inkling-Small ./thinkingmachines__Inkling-Small.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download thinkingmachines/Inkling-Small --revision 8cc5877b44d343f88b92086aa1fb72897950f06a --local-dir ./Inkling-Small
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("thinkingmachines/Inkling-Small", revision="8cc5877b44d343f88b92086aa1fb72897950f06a") model = AutoModelForImageTextToText.from_pretrained("thinkingmachines/Inkling-Small", revision="8cc5877b44d343f88b92086aa1fb72897950f06a", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve thinkingmachines/Inkling-Small --revision 8cc5877b44d343f88b92086aa1fb72897950f06a

License

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Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
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License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
2de45e1f0d8d387626aa063dccd54c1f519c21b2adec1c1fd597e451ae08d5db
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Provenance

From: thinkingmachines <huggingface.co/thinkingmachines>Newsgroups: alt.freeweights.textSubject: Inkling-SmallDate: 2026-11-15Commit: 8cc5877Signed: 2DC28CC7B26A283B ✓
Upstream
thinkingmachines/Inkling-Small
Pinned commit
8cc5877b44d343f88b92086aa1fb72897950f06a
Manifest
Inkling-Small.json · .minisig
Manifest sha256
09ac01838f1ba33e0801f1d667bc643f3dfdb6dbe414c4e981ee27c70ac0a61d
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:26 UTC
Generator
freeweights-manifest/0.1.0
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None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/thinkingmachines/Inkling-Small.json $ curl -O https://freeweights.allternit.com/manifests/thinkingmachines/Inkling-Small.json.minisig $ minisign -Vm Inkling-Small.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

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

Quantizations
1
Quantizations 1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download thinkingmachines/Inkling-Small
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. Inkling-Small.json. Provenance. Takedown history: none.
Source
huggingface.co/thinkingmachines/Inkling-Small

Variants

1 quantized or adapter build of Inkling-Small. Each has its own license check and page.

SubjectSizeParamsLicenseDate
Inkling-Small-GGUFHosted (signed) From: unsloth in alt.freeweights.text3.88 TB—Permissive2026-11-15

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