The Markdownified datasets are licensed under the original licenses of the individual documents. Please refer to StackExchange, Wikipedia, and arXiv for more information.
The Datashop Science QA dataset is licensed under CC-BY-SA 4.0.
Marin 32B follows a Llama-style transformer architecture with QK-Norm attention (matching the Qwen3 32B backbone) and works out-of-the-box with the Hugging Face Transformers library and other libraries that support Llama/Qwen-style causal language models.
We evaluate with EleutherAI's LM Eval Harness defaults across a standard suite. Numbers may differ from model cards or other evaluation harnesses due to prompt/format differences. “Average” is a simple mean over the shown tasks.
Model
Average
AGI Eval LSAT-AR
ARC Easy
ARC Challenge
BoolQ
CommonSense QA
COPA
HellaSwag
lambada_openai
OpenBookQA
PIQA
WinoGrande
WSC
MMLU
GPQA
BBH
MMLU Pro
HumanEval
GSM8K
MATH
Marin 32B (Mantis)
65.2
24.8
88.0
65.7
89.4
82.8
93.0
86.9
77.2
46.4
85.9
79.3
79.5
74.7
34.0
59.6
45.1
42.7
69.1
15.3
Marin 32B (Bison)
63.0
23.4
87.8
65.8
88.9
82.3
94.0
86.6
77.4
46.6
86.1
78.6
82.4
72.9
32.1
55.2
41.9
29.3
54.7
10.4
OLMo 2 32B Base
63.2
22.6
85.9
61.9
83.0
78.6
93.0
85.9
78.3
47.2
83.1
78.9
86.8
71.9
32.2
56.1
42.0
23.8
76.4
12.7
Qwen 2.5 32B Base
68.1
30.4
80.8
55.9
87.7
88.5
87.0
84.1
77.6
44.4
82.4
75.7
81.0
80.8
39.0
67.4
57.9
48.8
89.3
36.3
Gemma 3 27B PT
65.1
22.2
88.2
65.4
87.1
73.4
93.0
83.0
78.1
45.0
84.1
79.0
91.9
75.3
35.7
61.4
49.4
17.6
82.0
25.8
NVIDIA Nemotron Nano 12B v2 Base
68.6
28.7
83.6
60.6
84.8
76.1
85.0
81.4
72.9
45.8
82.8
74.4
85.4
77.9
36.6
62.0
53.1
59.2
84.1
68.3
The Mantis cooldown improves coding (HumanEval) and math (GSM8K, MATH) performance dramatically compared with the earlier Bison cooldown while maintaining competitive accuracy across general-language benchmarks.
Marin 32B uses the stanford-crfm/marin-tokenizer. It has the same vocabulary as Llama 3 but bundles a chat template into the base tokenizer for convenience.
Like any base language model or fine-tuned model without safety filtering, these models can be prompted to generate harmful or sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so users should consider the risks when applying this technology. Additionally, many statements from Marin or any LLM can be inaccurate, so responses should be verified.
Marin 32B has not undergone safety tuning or evaluation. We strongly recommend using this model with caution and considering the risks when applying this technology. In particular, this model is not intended for fully autonomous use.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("marin-community/marin-32b-base", revision="f19b346615212ff9a6d92c10861a9cdb100cbf97")
model = AutoModelForCausalLM.from_pretrained("marin-community/marin-32b-base", revision="f19b346615212ff9a6d92c10861a9cdb100cbf97", device_map="auto")
