Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models:

XL: Mathematically equivalent neural network, optimized with our DNN compiler.
L: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks.
M: Faster model, with accuracy degradation less than 1.5%.
S: The fastest model, with accuracy degradation less than 2%.

Goals of elastic models:

Provide flexibility in cost vs quality selection for inference
Provide clear quality and latency benchmarks
Provide interface of HF libraries: transformers and diffusers with a single line of code
Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT.
Provide the best models and service for self-hosting.

It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well.

Performance Graph

Inference

To infer our models, you just need to replace transformers import with elastic_models.transformers:

python

import torch
from transformers import AutoTokenizer
from elastic_models.transformers import AutoModelForCausalLM

# Currently we require to have your HF token
# as we use original weights for part of layers and
# model confugaration as well
model_name = "Qwen/Qwen2.5-14B-Instruct"
hf_token = ''
device = torch.device("cuda")

# Create mode
tokenizer = AutoTokenizer.from_pretrained(
    model_name, token=hf_token
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    token=hf_token,
    torch_dtype=torch.bfloat16,
    attn_implementation="sdpa",
    mode='S'
).to(device)
model.generation_config.pad_token_id = tokenizer.eos_token_id

# Inference simple as transformers library
prompt = "Describe basics of DNNs quantization."
messages = [
  {
    "role": "system",
    "content": "You are a search bot, answer on user text queries."
  },
  {
    "role": "user",
    "content": prompt
  }
]

chat_prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

inputs = tokenizer(chat_prompt, return_tensors="pt")
inputs.to(device)

with torch.inference_mode():
    generate_ids = model.generate(**inputs, max_length=500)

input_len = inputs['input_ids'].shape[1]
generate_ids = generate_ids[:, input_len:]
output = tokenizer.batch_decode(
    generate_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)[0]

# Validate answer
print(f"# Q:\n{prompt}\n")
print(f"# A:\n{output}\n")

System requirements:

GPUs: H100, L40s
CPU: AMD, Intel
Python: 3.10-3.12

To work with our models just run these lines in your terminal:

shell

pip install thestage
pip install 'thestage-elastic-models[nvidia]' --extra-index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple
pip install flash_attn==2.7.3 --no-build-isolation
pip uninstall apex

Then go to app.thestage.ai, login and generate API token from your profile page. Set up API token as follows:

thestage config set --api-token <YOUR_API_TOKEN>

Congrats, now you can use accelerated models!

Benchmarks

Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. The W8A8, int8 column indicates that we applied W8A8 quantization with int8 data type to all linear layers and used the same calibration data as for ANNA. The S model achieves practically identical speed but much higher quality, as ANNA knows how to improve quantization quality on sensitive layers!

Quality benchmarks

Metric/Model	S	M	L	XL	Original	W8A8, int8
arc_challenge	57.50	59.60	61.00	60.70	60.70	49.90
mmlu	78.10	79.10	79.80	79.80	79.80	66.40
piqa	81.30	81.60	81.70	81.30	81.30	73.30
winogrande	74.0	74.40	75.00	76.00	76.00	60.0

MMLU: Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics.
PIQA: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts.
Arc Challenge: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks.
Winogrande: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity.

Latency benchmarks

100 input/300 output; tok/s:

GPU/Model	S	M	L	XL	Original	W8A8, int8
H100	118	103	94	77	39	121
L40S	40	34	30	24	22	41

TheStageAI/Elastic-Qwen2.5-14B-Instruct

Elastic model: Qwen2.5-14B-Instruct. Fastest and most flexible models for self-serving.

Performance Graph

Inference

Benchmarks

Quality benchmarks

Latency benchmarks

Links

TheStageAI/
Elastic-Qwen2.5-14B-Instruct