Instructions to use zjr2000/SPES-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjr2000/SPES-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zjr2000/SPES-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zjr2000/SPES-7B")
model = AutoModelForCausalLM.from_pretrained("zjr2000/SPES-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zjr2000/SPES-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjr2000/SPES-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjr2000/SPES-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zjr2000/SPES-7B

SGLang

How to use zjr2000/SPES-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zjr2000/SPES-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjr2000/SPES-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zjr2000/SPES-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjr2000/SPES-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zjr2000/SPES-7B with Docker Model Runner:
```
docker model run hf.co/zjr2000/SPES-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SPES-7B

SPES-7B is a 7B-parameter Mixture-of-Experts (MoE) Large Language Model pretrained using SPES (SParse Expert Sync), a memory-efficient decentralized training framework.

This model was introduced in the paper: Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm.

Authors: Jinrui Zhang, Chaodong Xiao, Aoqi Wu, Xindong Zhang, Lei Zhang.

Model Details

Model name: SPES-7B
Model type: Causal language model (MoE)
Parameters: 7B
Architecture: Olmoe
Framework: SPES
License: Apache-2.0

Introduction

SPES (SParse Expert Sync) is designed for pretraining MoE LLMs across geographically distributed GPU nodes. It addresses memory and bandwidth constraints by training only a subset of experts per node, significantly lowering the individual memory footprint and eliminating the need for full-parameter transmission. SPES-7B achieves competitive performance with centrally trained models under similar computational budgets.

Project Links

GitHub: zjr2000/SPES
Paper (arXiv): 2602.11543
Model Collection: SPES Collection

Intended Use

This model is intended for research on:

Decentralized LLM pretraining paradigms.
Mixture-of-Experts (MoE) training and synchronization.
Evaluation of pretrained language models trained under constrained bandwidth conditions.

Citation

If you use this model, please cite the SPES paper:

@article{zhang2026pretraining,
  title={Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm},
  author={Zhang, Jinrui and Xiao, Chaodong and Wu, Aoqi and Zhang, Xindong and Zhang, Lei},
  journal={arXiv preprint arXiv:2602.11543},
  year={2026}
}