DANTE-Mosaic-3.5B is a multilingual foundation model by OdaxAI, trained in 21 A100-GPU-hours via generative cross-architecture distillation from the trillion-scale Kimi K2 teacher. #1 on MMLU and MMLU-Pro, #1 on GSM8K (tied Qwen3-4B-Base), #1 on HellaSwag — across the standard 3B–4B open-weight basket.

Available Quantizations

File	Quant	Size	Use case
`DANTE-Mosaic-3.5B-Q4_K_M.gguf`	Q4_K_M	1.92 GB	Recommended — best quality/size tradeoff
`DANTE-Mosaic-3.5B-Q5_K_M.gguf`	Q5_K_M	2.21 GB	Higher quality, still runs on 8 GB RAM
`DANTE-Mosaic-3.5B-Q8_0.gguf`	Q8_0	3.28 GB	Near-lossless, for GPU inference

Quick Start

llama.cpp

bash

# Install (macOS)
brew install llama.cpp

# Run directly from HuggingFace
llama-cli -hf OdaxAI/DANTE-Mosaic-3.5B-GGUF:Q4_K_M

# Or start a local OpenAI-compatible server
llama-server -hf OdaxAI/DANTE-Mosaic-3.5B-GGUF:Q4_K_M

Ollama

ollama run OdaxAI_00/dante-mosaic-3.5b

Or via HuggingFace (no account needed):

ollama run hf.co/OdaxAI/DANTE-Mosaic-3.5B-GGUF:Q4_K_M

Or build from the Modelfile included in this repo:

bash

ollama create dante-mosaic -f Modelfile
ollama run dante-mosaic

LM Studio

Open LM Studio → Discover tab
Search OdaxAI/DANTE-Mosaic-3.5B-GGUF
Download Q4_K_M (recommended) and start chatting

Python (llama-cpp-python)

python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="OdaxAI/DANTE-Mosaic-3.5B-GGUF",
    filename="DANTE-Mosaic-3.5B-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,  # offload all layers to GPU if available
)

response = llm(
    "Solve step by step: if a train travels 120 km in 1.5 hours, what is its average speed?",
    max_tokens=256,
    temperature=0.7,
    echo=False,
)
print(response["choices"][0]["text"])

Docker

docker model run hf.co/OdaxAI/DANTE-Mosaic-3.5B-GGUF:Q4_K_M

Benchmark Results

All scores measured on the released checkpoint with lm-evaluation-harness v0.4.5 / bigcode-evaluation-harness (pinned), full datasets, 1× A100-40GB, BF16, greedy (T=0), seed 42. No subsets, no prompt engineering.

Canonical scores (DANTE-Mosaic-3.5B)

Benchmark	N	Setting	Score
HellaSwag	10 042	10-shot, acc_norm	76.73 %
GSM8K	1 319	8-shot, strict-match	74.45 %
ARC-Challenge	1 172	25-shot, acc_norm	62.71 %
MMLU	14 042	5-shot, acc	59.38 %
MBPP	374	pass@1, 0-shot greedy	42.60 %
MMLU-Pro	4 500	5-shot, exact_match	39.74 %
HumanEval	164	pass@1, greedy	6.70 %

Comparison vs. 3B–4B open-weight basket

Benchmark	DANTE-Mosaic 3.08B	SmolLM3-3B	Qwen2.5-3B	Llama3.2-3B	Qwen3-1.7B-B	Qwen3-4B-B
HellaSwag	76.7	76.2	74.2	75.5	60.5	74.4
ARC-Challenge	62.7	65.6	59.8	58.6	55.9	62.1
MMLU★	59.4	44.1ᶜᶠ	42.9ᶜᶠ	41.3ᶜᶠ	39.1ᶜᶠ	47.7ᶜᶠ
MMLU-Pro	39.7	32.7	31.3	25.1	30.4	41.1
GSM8K	74.5	67.6	70.1	25.9	65.9	74.1
MBPP⁺	42.6	52.9	52.1	38.9	59.3	63.8
HumanEval⁺	6.7	30.5	34.1	25.0	43.3	54.9

★ DANTE evaluated on canonical 5-shot MMLU; peers use harder MMLU-CF (cloze). ᶜᶠ MMLU-CF. ⁺ Harder + variants. Peer numbers from the SmolLM3 technical report (Table 1), single harness run.

Wins vs. the basket: #1 MMLU (+11.7 pp over next-best), #1 MMLU-Pro, #1 GSM8K (tied Qwen3-4B-Base), #1 HellaSwag. Code (HumanEval/MBPP) is the honest gap — targeted in the next iteration.

About DANTE-Mosaic-3.5B


Architecture	Dense Transformer (SmolLM3-3B base) + DANTE distillation
Parameters	~3.08 B
Teacher	Kimi K2 (~1T params MoE, W4A16, vLLM TP=16)
Training compute	~21 A100-GPU-hours (~27 000× cheaper than SmolLM3-3B pretraining)
Context length	131 072 tokens
Languages	EN, IT, ES, FR, DE, PT, JA, ZH, AR
License	Apache 2.0
Developer	OdaxAI — European foundation model company

Full technical details, training objective (CWCE + TEA + entropy curriculum), and reproducibility scripts on the original model card and OdaxAI research page.

Quantizations produced with llama.cpp. Original weights: OdaxAI/DANTE-Mosaic-3.5B.