What are the hardware requirements for Vigogne 2 13B Instruct?

Vigogne 2 13B Instruct requires approximately 25.9 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vigogne 2 13B Instruct and how large is it?

Vigogne 2 13B Instruct is developed by bofenghuang, a model with 13b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Vigogne 2 13B Instruct perform on standard benchmarks?

Vigogne 2 13B Instruct has the following published scores: MMLU 55.92. Compare against reference models on this page or on the LLM Explorer leaderboards.

Vigogne 2 13B Instruct by bofenghuang — VRAM 25.9GB, 4K context

Name: Vigogne 2 13B Instruct
Author: bofenghuang

Vigogne 2 13B Instruct is an open-source language model by bofenghuang. Features: 13b LLM, VRAM: 25.9GB, Context: 4K, Instruction-Based, HF Score: 55.1, LLM Explorer Score: 0.12, Arc: 61.2, HellaSwag: 83.3, MMLU: 55.9, TruthfulQA: 51.1, WinoGrande: 77.4, GSM8K: 2.1.

Fr Instruct Llama Llama2 Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/bofenghuang/vigogne-2-13b-instruct

Vigogne 2 13B Instruct Benchmarks

ARC: 61.18 vs 96.7 (so35)^-36.7%

HellaSwag: 83.25 vs 95.3 (gpt4)^-12.6%

MMLU: 55.92 vs 88.3 (so35)^-36.7%

TruthfulQA: 51.08 vs 59 (gpt4)^-13.4%

WinoGrande: 77.35 vs 87.5 (gpt4)^-11.6%

GSM8K: 2.05 vs 96.4 (so35)^-97.9%

LLME Score: 0.11949

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Vigogne 2 13B Instruct (bofenghuang/vigogne-2-13b-instruct)

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Vigogne 2 13B Instruct Parameters and Internals

Model Type

text generation, instruction-following

Use Cases

Areas:

research, applications utilizing French instructions

Limitations:

model might generate harmful or biased content, incorrect information or unhelpful answers

Additional Notes

The model is designed for instruction-following in the French language.

Supported Languages

fr (native)

LLM Name	Vigogne 2 13B Instruct
Repository 🤗	https://huggingface.co/bofenghuang/vigogne-2-13b-instruct
Model Size	13b
Required VRAM	25.9 GB
Updated	2026-04-13
Maintainer	bofenghuang
Model Type	llama
Instruction-Based	Yes
Model Files	1.9 GB: 1-of-14 1.9 GB: 2-of-14 1.9 GB: 3-of-14 1.9 GB: 4-of-14 1.9 GB: 5-of-14 1.9 GB: 6-of-14 1.9 GB: 7-of-14 1.9 GB: 8-of-14 1.9 GB: 9-of-14 1.9 GB: 10-of-14 1.9 GB: 11-of-14 1.9 GB: 12-of-14 1.9 GB: 13-of-14 1.2 GB: 14-of-14 1.9 GB: 1-of-14 1.9 GB: 2-of-14 1.9 GB: 3-of-14 1.9 GB: 4-of-14 1.9 GB: 5-of-14 1.9 GB: 6-of-14 1.9 GB: 7-of-14 1.9 GB: 8-of-14 1.9 GB: 9-of-14 1.9 GB: 10-of-14 1.9 GB: 11-of-14 1.9 GB: 12-of-14 1.9 GB: 13-of-14 1.2 GB: 14-of-14
Supported Languages	fr
Model Architecture	LlamaForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.32.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Quantized Models of the Vigogne 2 13B Instruct

Model	Likes	Downloads	VRAM
Vigogne 2 13B Instruct GGUF	3	129	5 GB
Vigogne 2 13B Instruct GGML	6	6	5 GB
Vigogne 2 13B Instruct GPTQ	3	8	7 GB

Best Alternatives to Vigogne 2 13B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
NexusRaven V2 13B	16K / 26 GB	80	470
CodeLlama 13B Instruct Hf	16K / 26 GB	5282	159
CodeLlama 13B MORepair	16K / 26 GB	12	2
CodeLlama 13B Instruct Hf	16K / 26 GB	234	27
TableLLM 13B	16K / 26 GB	306	32
NexusRaven 13B	16K / 26 GB	17	105
Panda Coder 13B	16K / 26 GB	20	14
... Llama 2 13B Instruct Text2sql	16K / 26 GB	42	28
Gen Sim	16K / 0.3 GB	8	3
Llama 3 13B Instruct Ft	8K / 26.1 GB	10	2

Note: green Score (e.g. "73.2") means that the model is better than bofenghuang/vigogne-2-13b-instruct.

Rank the Vigogne 2 13B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Release v20260328a

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Vigogne 2 13B Instruct by bofenghuang