bineric/lynx-instruct-30b

Bineric Lynx Instruct 30B

A European large language model with exceptional Nordic language performance.

Parameters	30B total, ~3B active
Architecture	Qwen3 MoE (128 experts, 8 active)
Context Length	262K tokens
Base Model	Qwen3-30B-A3B-Instruct
Languages	Norwegian (Bokmål/Nynorsk), Swedish, Danish, Icelandic + 100+ via base

About Bineric

Bineric is an AI company based in Oslo, Norway, built from a European perspective. We started Bineric to make AI usable for organizations that care about governance, language, and where their systems and data actually live.

Lynx is our flagship model — designed to serve European users with strong multilingual support and exceptional Nordic language performance.

Overview

Lynx is built on Qwen3-30B's efficient Mixture-of-Experts architecture. It retains strong multilingual capabilities across 100+ languages including all major European languages, while being specifically fine-tuned and rigorously evaluated on Nordic languages (Norwegian, Swedish, Danish, Icelandic) where it demonstrates exceptional results.

Key features:

• Strong European language support inherited from Qwen3 base model
• Fine-tuned and optimized for Nordic language understanding and generation
• Efficient MoE architecture: only 3B parameters active per token
• Available in 8-bit and 4-bit quantized variants for flexible deployment
• 262K context window for long-document processing

Try Lynx

Lynx is available through multiple channels:

Access Method	Link	Best For
Chatbot	chat.bineric.com	Interactive conversations, quick testing
API	bineric.com/platform	Production integrations, programmatic access
Hugging Face	This repository	Self-hosting, fine-tuning, research

Evaluation Results

Evaluated using EuroEval benchmark framework (March 2026).

Note: While Lynx supports all European languages via its Qwen3 base, we have rigorously evaluated performance on Nordic languages. Benchmarks for additional European languages coming soon.

Nordic Language Performance

Language	Overall Score	Best Task	Score
Danish	79.3%	Citizen Tests (Knowledge)	79.3%
Swedish	76.9%	European Values	76.9%
Norwegian	71.0%	NER Nynorsk	71.0%
Icelandic	65.1%	Summarization	65.1%

Task Performance by Language

Norwegian (8-bit)

Task	Dataset	Metric	Score
Sentiment	NoReC	MCC	51.0%
NER (Bokmål)	NorNE-nb	F1	65.7%
NER (Nynorsk)	NorNE-nn	F1	71.0%
Reading Comprehension	NorQuAD	F1	61.2%
Summarization	NoSammendrag	BERTScore	63.4%
Common Sense	NorCommonSenseQA	MCC	69.3%
Knowledge	NRK Quiz QA	MCC	35.3%

Danish (8-bit)

Task	Dataset	Metric	Score
Sentiment	AngryTweets	MCC	54.8%
NER	DANSK	F1	53.8%
Reading Comprehension	MultiWikiQA-da	F1	72.2%
Summarization	Nordjylland News	BERTScore	65.2%
Common Sense	HellaSwag-da	MCC	67.7%
Knowledge	Danish Citizen Tests	MCC	79.3%
Idioms	Danske Talemåder	MCC	64.9%

Swedish (8-bit)

Task	Dataset	Metric	Score
Sentiment	SweReC	MCC	34.5%
NER	SUC3	F1	65.0%
Reading Comprehension	MultiWikiQA-sv	F1	72.4%
Summarization	SweDN	BERTScore	65.9%
Common Sense	HellaSwag-sv	MCC	58.3%
Knowledge	MMLU-sv	MCC	53.9%
European Values	VaLEU-sv	MCC	76.9%

Icelandic (8-bit)

Task	Dataset	Metric	Score
NER	MIM-GOLD-NER	F1	63.6%
Reading Comprehension	NQiI	F1	58.6%
Summarization	RRN	BERTScore	65.1%
Knowledge	Icelandic Knowledge	MCC	28.2%
Common Sense	Winogrande-is	MCC	9.7%

Quantization Comparison (Norwegian)

8-bit quantization consistently outperforms 4-bit by ~2% on average.

Task	4-bit	8-bit	Delta
Sentiment (NoReC)	49.7%	51.0%	+1.3%
NER Bokmål	65.1%	65.7%	+0.6%
NER Nynorsk	69.9%	71.0%	+1.1%
Reading Comp	58.9%	61.2%	+2.3%
Summarization	63.1%	63.4%	+0.3%
Common Sense	68.5%	69.3%	+0.8%
Linguistic Accept.	29.8%	36.4%	+6.6%

Strengths & Limitations

Strengths

• Named Entity Recognition: Consistently strong across all languages (63-71% F1)
• Reading Comprehension: Excellent for Danish and Swedish (72%+)
• Knowledge Tasks: Outstanding on Danish Citizen Tests (79.3%)
• Summarization: Stable 63-66% BERTScore across all languages

Limitations

• Linguistic Acceptability: Grammatical judgment tasks are weak (10-36% MCC)
• Icelandic Common Sense: Winogrande-is performance is low (9.7%)
• Norwegian Idioms: Room for improvement (17-19% MCC)

Quantization Options

Variant	Size	Quality	Use Case
bfloat16	~60GB	Best	Research, high-end GPUs
8-bit	~30GB	~1-2% loss	Production (A10/L4 GPU)
4-bit	~16GB	~3-5% loss	Cost-optimized (T4 GPU)

Usage

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "bineric/lynx-instruct-30b",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("bineric/lynx-instruct-30b")

messages = [
    {"role": "user", "content": "Hva er hovedstaden i Norge?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Thinking Mode

Lynx supports extended thinking for complex reasoning tasks:

messages = [
    {"role": "user", "content": "Forklar forskjellen mellom bokmål og nynorsk."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Enable reasoning mode
)

vLLM Deployment

vllm serve bineric/lynx-instruct-30b \
    --tensor-parallel-size 1 \
    --max-model-len 32768 \
    --quantization awq  # For 4-bit

Model Architecture

Qwen3 MoE Architecture
├── Total Parameters: 30.5B
├── Active Parameters: ~3B per token
├── Hidden Layers: 48
├── Hidden Size: 2048
├── Attention Heads: 32
├── KV Heads: 4 (Grouped Query Attention)
├── Experts: 128 total
├── Active Experts: 8 per token
├── Vocab Size: 151,936
└── Context Length: 262,144 tokens

Training

Lynx is fine-tuned from Qwen3-30B-A3B-Instruct with additional training on Nordic language data to improve performance on Norwegian, Swedish, Danish, and Icelandic tasks.

Citation

@misc{bineric2026lynx,
  title={Bineric Lynx: A European Large Language Model},
  author={Bineric AI},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/bineric/lynx-instruct-30b}
}

Links

• Base Model: Qwen3-30B-A3B-Instruct

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Built with care in Oslo by Bineric