Apriel

/ˈɑː.pri.əl/

Table of Contents

1. Model Summary
2. Evaluation
3. Intended Use
4. Limitations
5. Security and Responsible Use
6. License
7. Citation

Model Summary

Apriel is a family of models built for versatility, offering high throughput and efficiency across a wide range of tasks.

Apriel-5B-Base

Apriel-5B-base is a decoder-only transformer trained on 4.5T+ tokens of data. It is the first release in the Apriel model family, designed to support research on foundation models. Apriel-5B-base achieves strong performance across common benchmarks for models under 5B parameters.

Apriel-5B-Instruct

Apriel-5B-Instruct is built on top of Apriel-5B-base using continual pretraining (CPT), supervised finetuning (SFT), and post-training alignment with DPO and RLVR.

Both CPT and SFT stages involved training multiple domain-biased variants with overlapping datasets (e.g., instruction, code, math). These were then merged to form a more general-purpose model before alignment. The final model is aligned for instruction following, reasoning, and safety-aware dialogue.

The y-axis shows average downstream benchmark scores. Throughput (x-axis) was measured using vLLM with batch size 8, 256 input tokens, and 32 output tokens.

How to Use

pip install transformers

Running the Base model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "ServiceNow-AI/Apriel-5B-Base"
device = "cuda"  # or "cpu"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16).to(device)

inputs = tokenizer.encode("Snow is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 9664.14 MB

Running the Instruct model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "ServiceNow-AI/Apriel-5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32
).to(device)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant that provides accurate and concise information."},
    {"role": "user", "content": "Tell me about artificial intelligence"}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(device)

generation_params = {
    "max_new_tokens": 512,
    "temperature": 0.2,
    "top_p": 0.9,
    "do_sample": True
}

outputs = model.generate(**inputs, **generation_params)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Chat Template

System message here (optional)
User message here
Assistant response here

If no system message is provided, the model inserts a blank system prompt to maintain format structure. The model supports structured interaction patterns, including tool calling and reasoning steps for more advanced workflows.

Evaluation

Evaluations were conducted using lm-eval-harness and evalchemy.

Apriel-5B-Base

Apriel-5B-Instruct