Elastic DeepSeek R1 Distill Qwen 7B

🚀 Elastic model: DeepSeek-R1-Distill-Qwen-7B. Fastest and most flexible models for self-serving.

Elastic models are designed to offer the fastest and most flexible solutions for self - serving. They are produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA enables users to control model size, latency, and quality with a simple slider movement. For each base model, ANNA generates a series of optimized models:

• XL: A mathematically equivalent neural network optimized by our DNN compiler.
• L: A near - lossless model with less than 1% degradation on corresponding benchmarks.
• M: A 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.
• Offer clear quality and latency benchmarks.
• Provide an interface with HF libraries (transformers and diffusers) in a single line of code.
• Support a wide range of pre - compiled hardware without the need for JIT.
• Provide the best models and services for self - hosting.

⚠️ Important Note

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.

🚀 Quick Start

📦 Installation

To work with our models, you need to install the necessary packages. Run the following commands in your terminal:

pip install thestage
pip install elastic_models[nvidia]\
 --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
 --extra-index-url https://pypi.nvidia.com\
 --extra-index-url https://pypi.org/simple

pip install flash_attn==2.7.3 --no-build-isolation
pip uninstall apex

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

thestage config set --api-token <YOUR_API_TOKEN>

💻 Usage Examples

Basic Usage

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

# Currently we require to have your HF token
# as we use original weights for part of layers and
# model confugaration as well
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
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

📚 Documentation

Model Information

Benchmarks

Benchmarking is a crucial step in model acceleration. We strive 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

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

Latency benchmarks

100 input/300 output; tok/s:

📄 Links

• Platform: app.thestage.ai
• Subscribe for updates: TheStageAI X
• Contact email: contact@thestage.ai