NLPnorth/snakmodel-7b-instruct

Model Details

SnakModel is a 7B-parameter model specifically designed for the Danish language. This is the instruction-tuned variant: SnakModel-7B (instruct). Our models build upon Llama 2, which we continuously pre-train on a diverse collection of Danish corpora comprising 350M documents and 13.6B words, before tuning it on 3.7M Danish instruction-answer pairs.

Model Developers

NLPnorth research unit at the IT University of Copenhagen, Denmark.

Variations

SnakModel comes as an instruction-tuned, and a base version. In addition, each model includes intermediate checkpoints (under model revisions).

Input

Text only, with instructions following the [INST] {instruction} [/INST] template.

Quickstart:

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "NLPnorth/snakmodel-7b-instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Hvor ligger IT Universitet?"
messages = [
    {"role": "system", "content": "Du er Snakmodel, skabt af IT-Universitetet i København. Du er en hjælpsom assistent."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=20
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Output

Text only.

Model Architecture

SnakModel is an auto-regressive, transformer-based language model. The instruct version uses supervised fine-tuning (SFT) to enable instruction following in Danish.

Model Dates

SnakModel was trained between January 2024 and September 2024.

License

This model follows the original Llama 2 license agreement.

Research Paper

[Released in Q1 2025]

Intended Use & Limitations

Intended Use Cases

SnakModel is intended for use in Danish. The instruction-tuned variant is intended for assistant-like chat.

The instruct variant follows the Llama 2 (chat) instruction template, in which instructions are encapsulated in special tokens, i.e., [INST] {instruction} [/INST].

Limitations

SnakModel variants are fine-tuned on Danish data. As such, the use in other languages falls out-of-scope. While we found SnakModel to be more proficient in Danish, than other Llama 2-based models, it still frequently generates factually incorrect output. Make sure to carefully evaluate and weigh these factors before deploying the model. In addition, make sure to adhere to the original Llama 2 license agreement.

Hardware and Software

Training Factors

SnakModel is trained on private infrastructure with one node, containing four NVIDIA A100-PCIe 40GB GPUs. The node has an AMD Epyc 7662 128 Core Processor and 1TB of RAM.

Carbon Footprint

Total training time accounted to 8,928 GPU hours, with an average carbon efficiency at 0.122kg CO2eq / kWh. This is equivalent to 272.3kg CO2eq emitted, based on the Machine Learning Impact calculator.

Training Data

Overview

SnakModel was continuously pre-train on a diverse collection of Danish corpora comprising 350M documents and 13.6B words. The instruct version is further tuned on 3.7M Danish instruction-answer pairs.

[Details to follow in Q1 2025]

Data Freshness

The pre-training data has a cutoff of January 2024.

Evaluation Results

[Released in Q1 2025]

Citation

[Released in Q1 2025]