metadata

license: apache-2.0
base_model:
  - mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text2text-generation

Elastic models

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.

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.

Inference

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

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

model_name = "mistralai/Mistral-7B-Instruct-v0.3"
token = ''

device = torch.device("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)

model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    token=token,
    cache_dir="/mnt/rnd/huggingface_cache",
    torch_dtype=torch.bfloat16,
    attn_implementation="sdpa"
).to(device)
model.generation_config.pad_token_id = tokenizer.eos_token_id

prompt = "Describe basics of DNNs quantization."
inputs = tokenizer(prompt, return_tensors="pt")
inputs.to(device)

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

System requirements

GPUs: H100, L40s

OS: Linux #TODO

Python: 3.10-3.12

Installation

pip install thestage
pip install elastic_models

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

For quality evaluation we have used: #TODO link to github

Metric/Model	S	M	L	XL	Original	W8A8, int8
MMLU	0	0	0	0	0	0
PIQA	0	0	0	0	0	0
Arc Challenge	0	0	0	0	0	0
Winogrande	0	0	0	0	0	0

MMLU: Evaluates/shows ...

Arc Challenge: Evaluates/shows ...

PIQA: Evaluates/shows ...

Latency benchmarks

We have profiled models in different scenarios:

100 input/300 output tok/s:

GPU/Model	S	M	L	XL	Original	W8A8, int8
H100	189	0	0	0	48	0
L40s	79	0	0	0	42	0

1000 input/1000 output tok/s:

GPU/Model	S	M	L	XL	Original	W8A8, int8
H100	189	0	0	0	48	0
L40s	79	0	0	0	42	0

TheStageAI
/

Elastic-Mistral-7B-Instruct-v0.3