Inkling-2-Small-GGUF

Run Inkling-2-Small via the Unsloth Desktop or read our Inkling 2 guide#

See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://unsloth.ai/docs/models/inkling-2

Image: Inkling-2-Small Unsloth GGUF KLD vs size Image: Inkling-2-Small Unsloth GGUF top-1 accuracy vs size

GGUF files#

• UD-* folders: Unsloth Dynamic quants, imatrix-calibrated (imatrix_unsloth.gguf_file).
• BF16: lossless conversion: the original NVFP4 experts kept bit-exact (layers 0-39), every other weight BF16.
• UD-Q8_K_XL: the original NVFP4 experts kept bit-exact (layers 0-39), every other weight 8-bit.
• mmproj-BF16.gguf / mmproj-F16.gguf / mmproj-F32.gguf: vision projector, pass with --mmproj for image inputs.
• The chat template is embedded in every GGUF; use --jinja.

KLD and top-token agreement are measured against the BF16 GGUF (the original NVFP4 experts kept as is, every other weight BF16) on 64 x 4096 tokens of wikitext-2, with llama.cpp's default F16 KV cache on both sides.

Running#

Inkling 2 Small uses a new inkling2 architecture, so it needs a llama.cpp build with Inkling 2 support. Builds that only support the original Inkling will not load it.

1. Install llama.cpp with Inkling 2 support

Follow our Inkling 2 guide to set up llama.cpp with Inkling 2 support, or use the Unsloth Desktop app.

2. Download a quant and the vision projector

pip install -U huggingface_hub
hf download unsloth/Inkling-2-Small-GGUF \
    --include "UD-IQ2_XXS/*" "mmproj-F16.gguf" \
    --local-dir Inkling-2-Small-GGUF

Keep all shards in the same folder and pass the first one to llama.cpp; the rest load automatically.

3. Run

./llama.cpp/llama-server \
    -m Inkling-2-Small-GGUF/UD-IQ2_XXS/Inkling-2-Small-UD-IQ2_XXS-00001-of-00005.gguf \
    --mmproj Inkling-2-Small-GGUF/mmproj-F16.gguf \
    --jinja -ngl 99 -c 32768 \
    --temp 1.0 --top-p 1.0 --top-k 0 --min-p 0.0 \
    --chat-template-kwargs '{"reasoning_effort":"high"}'

Open http://localhost:8080 to chat and upload images. Lower -ngl if the model does not fit in VRAM, or use -ngl 0 for CPU inference. For text-only use, omit --mmproj. reasoning_effort accepts none, minimal, low, medium, high (default), xhigh and max (same as xhigh).

Inkling 2 Small#

Image: image

NVFP4 | MXFP8 | Playground | Tinker Cookbook | Acceptable Use

1. General Information#

Inkling 2 Small is a general-purpose multimodal model that accepts text and image 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 visual 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 2 Small on the Tinker Playground or access via API using the Tinker Cookbook. To fine-tune Inkling 2 Small with Tinker, also install the tml-renderers package.

Inkling 2 Small supports local inference using:

• SGLang (recipe)
• vLLM (recipe)
• TokenSpeed (recipe)
• Unsloth (recipe)

Inkling 2 Small can also be post-trained with:

• Miles
• SkyRL
• Snowflake Cortex Training

API access is also available through third party inference providers, including:

• NVIDIA
• Fireworks
• Together
• Baseten
• Nebius
• Modal
• Cloudflare

Required hardware#

The model is distributed in two checkpoint formats.

The main NVFP4 checkpoint requires a GPU cluster with at least 500GB of aggregated VRAM. This can be met with either of the following example configurations:

• 2x NVIDIA B300 GPUs
• 4x NVIDIA H200 GPUs

The alternative MXFP8 checkpoint requires a GPU cluster with at least 800GB of aggregated VRAM. This can be met with either of the following example configurations:

• 4x NVIDIA B300 GPUs
• 8x NVIDIA H200 GPUs

The model was pretrained, midtrained and post-trained in NVFP4, so this is the native precision for the model. We recommend the NVFP4 checkpoint for all use-cases, and we're confident that it's the best option. The MXFP8 has a very marginal 1–2 percentage points gain over the NVFP4 checkpoint over evals, but we do not think it is meaningful for any practical use-cases.

Required software#

Running the model directly on GPU hardware requires an inference deployment framework — either SGLang, vLLM, TokenSpeed, Unsloth, or Hugging Face, along with all of their respective dependency libraries.

3. Model Properties#

Model type#

Multimodal autoregressive transformer

Architecture type#

A 48-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) feed-forward backbone: each token is routed to 8 of 384 experts. Attention is a hybrid of local and global layers. The model is natively multimodal — images are encoded via a vision transformer — with image and text inputs projected into a shared hidden space and processed jointly by the decoder.

Parameters#

594B total, 17B active

Numerics support#

MXFP8 and NVFP4

Input modalities#

Inkling 2 Small accepts text input in UTF-8 encoding and image input in any pixel-based format (with each dimension ideally between 40px and 4096px for optimal performance). It supports a context window of up to 1M tokens.

Output modalities#

Inkling 2 Small generates output as UTF-8 encoded text.

4. Training#

Training data includes a broad variety of content types, including text, images, video. Training data for the model was drawn from publicly available sources, acquired from third-parties, generated internally, 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 Flash | Open Weights Pro | Closed Weights Flash

| | Inkling 2 Small | GLM-5.3-Flash | MiMo V2.6 Flash | MiniMax-M3.1-Flash | DeepSeek-V4.1-Flash-1017 | Muse Spark Open | GLM 5.3 | MiMo V2.6 Pro | Mistral Large 4 | Kimi K3 | GPT-6 Luna | Gemini 3.8 Flash | Claude Haiku 5.5

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

| Params (B) activated / total | 17/594 | 18/320 | 15/309 | 23/428 | 16/552 | 42/816 | 40/753 | 42/1020 | 49/1050 | 104/2800 | – | – | –

| Third Party Index | | | | | | | | | | | | |

| AA Index (v5.0) | 30.6 | 27.8 | 26.7 | 29.1 | 32.6 | 24.9 | 31.7 | 29.9 | 26.1 | – | 27.9 | 30.8 | 36.5

| Vals Index (v2.2) | 56.1% | 50.9% | 54.8% | 53.8% | 61.3% | 48.6% | 59.7% | 57.8% | 56.5% | 53.0% | 52.3% | 56.0% | 58.7%

| Coding | | | | | | | | | | | | |

| FrontierCode v2.0 stirrup v0.2.1 | 25.4% | 17.6% | 21.6% | 20.8% | 28.5% | 7.6% | 21.6% | 24.0% | 15.0% | 17.3% | 25.2% | 18.7% | 34.9%

| DeepSWE v1.1 mini-swe-agent v2.4.6 | 72.1% | 63.4% | 67.9% | 73.7% | 77.0% | 60.0% | 69.0% | 71.9% | 61.7% | 68.5% | 66.6% | 73.8% | 59.9%

| Terminal Bench Science v0.1 mini-swe-agent v2.4.6 | 5.7% | 4.8% | 4.3% | 3.3% | 12.9% | 1.9% | 9.5% | 5.7% | 4.3% | 8.1% | 8.6% | 10.0% | 20.0%

| Vibe Code Bench v1.1 openhands (vals) | 81.6% | 30.8% | 79.0% | 73.9% | 87.7% | 76.2% | 78.1% | 85.2% | 81.6% | 85.0% | 81.7% | 78.7% | 90.4%

| SWE-Atlas v1.1 mini-swe-agent v2.4.6 | 61.9% | – | – | – | 62.1% | 42.3% | – | – | – | 61.0% | 50.8% | 57.3% | 59.4%

| SWEBench Pro v2 (Hard) mini-swe-agent v2.4.6 | 86.3% | 82.8% | 89.7% | 84.3% | 89.7% | 82.4% | 84.3% | 89.2% | 83.8% | 88.2% | 88.7% | 58.8% | 52.9%

| Sudo L7 | 14.1% | – | – | – | – | – | 9.4% | – | 13.9% | 12.2% | – | 10.6% | –

| Autoresearch | | | | | | | | | | | | |

| PostTrainBench v1.2 Inkling 2 Small in Claude Code | 31.9% | 36.8% | – | – | 30.5% | – | 34.5% | – | – | 31.5% | – | – | –

| PMPP-Hard (AUC, 0–10h) pi v0.83.0 | 494.27 | – | – | – | 497.36 | – | 430.96 | – | 478.39 | 453.58 | – | – | –

| BioMysteryBench terminus v2 | 70.0% | – | 69.3% | – | – | – | – | – | – | 72.6% | 61.5% | 62.2% | 70.4%

| Search and Tools | | | | | | | | | | | | |

| BrowseComp Inkling 2 Small with compaction | 90.2% | 76.0% | 78.1% | 69.5% | 87.8% | 68.7% | 75.1% | – | 84.5% | 88.4% | 83.6% | 89.4% | 80.2%

| DeepSearchQA Inkling 2 Small with compaction | 90.9% | 84.3% | 86.0% | 82.7% | 84.5% | 80.4% | 84.7% | 90.0% | 84.4% | 95.0% | 83.5% | 90.1% | 83.2%

| MCP Atlas Full / Public | 85.4/85.0% | –/82.0% | –/62.9% | –/80.4% | –/83.2% | 89.2/87.5% | 84.2/83.6% | –/84.4% | –/84.5% | 82.3/84.2% | –/77.8% | –/87.5% | –/81.0%

| Knowledge Work | | | | | | | | | | | | |

| AA-Briefcase v1.1 | 1527 | 1451 | 1487 | 1476 | 1570 | 1279 | 1503 | 1512 | 1348 | 1497 | 1337 | 1203 | 1574

| GDPVal-AA v2.1 | 1630 | 1649 | 1614 | 1465 | 1746 | 1401 | 1657 | 1689 | 1384 | 1543 | 1444 | 1445 | 1631

| JobBench | 70.5% | 60.7% | 61.2% | 67.5% | 68.5% | 61.8% | 61.4% | 62.0% | 55.2% | 54.3% | 49.0% | 63.4% | 39.2%

| DAYJOB (Healthcare) | 6.4% | 1.2% | – | – | 6.0% | – | 4.4% | – | 3.6% | 4.0% | 1.2% | 2.0% | –

| Enterprise Operations | | | | | | | | | | | | |

| EnterpriseOps-Gym | 46.8% | 33.2% | 29.0% | 48.9% | 43.6% | 43.1% | 36.4% | 38.0% | 44.9% | 45.3% | 24.9% | 45.7% | 25.1%

| EnterpriseBench-CoreCraft | 71.8% | 46.7% | – | – | 58.5% | – | 51.8% | – | 47.7% | 43.1% | 43.1% | 58.5% | –

| Finance Work | | | | | | | | | | | | |

| Finance Agent v2.1 | 67.5% | 60.6% | 63.3% | 58.8% | 62.9% | 60.7% | 65.0% | 64.3% | 61.7% | 60.0% | 56.8% | 65.8% | 65.4%

| Excel Modeling Benchmark v1.1 | 69.5% | 66.3% | 68.9% | 67.8% | 69.2% | 62.0% | 69.1% | 67.4% | 68.7% | 71.3% | 70.0% | 72.9% | 65.5%

| Tax Agent Bench | 65.8% | 62.0% | 59.9% | 62.7% | 74.9% | 52.1% | 73.1% | 64.9% | 67.5% | 68.7% | 58.9% | 66.8% | 62.2%

| Apex-Accounting v1.1 pass@1 | 14.5% | 12.3% | – | 13.5% | 12.5% | 13.8% | 13.9% | – | 12.0% | 9.5% | 10.8% | 15.5% | 15.3%

| DAYJOB (Finance) | 14.3% | 7.3% | – | – | 12.5% | – | 11.0% | – | 4.8% | 5.5% | 6.8% | 4.5% | –

| DeepFinance | 29.8% | – | – | – | – | – | 27.5% | – | 21.5% | – | 26.4% | – | 40.0%

| Diligence Bench Best harness | 58.1% | 48.3% | – | 51.6% | 50.4% | 59.0% | 49.4% | 51.8% | 51.5% | 50.9% | 42.1% | 51.1% | 40.8%

| AA-AnalystAgent | 51.2% | 25.0% | – | – | – | – | 38.8% | 37.5% | – | 38.8% | 43.8% | 46.3% | 53.8%

| Tau 3 Banking v1.0.1 | 40.4% | 47.2% | – | – | – | – | – | – | – | 33.4% | – | – | –

| ATLAS-Finance v1.1 | 6.0% | – | – | – | – | – | 3.0% | – | – | 3.7% | – | 3.7% | –

| Legal Work | | | | | | | | | | | | |

The publisher's README is longer; it is cut here. Read the rest at the source.