Instructions to use zjr2000/SPES-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjr2000/SPES-2B with Transformers:

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

pipe = pipeline("text-generation", model="zjr2000/SPES-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zjr2000/SPES-2B")
model = AutoModelForCausalLM.from_pretrained("zjr2000/SPES-2B")
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

vLLM

How to use zjr2000/SPES-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjr2000/SPES-2B"
# 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-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zjr2000/SPES-2B

SGLang

How to use zjr2000/SPES-2B 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-2B" \
    --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-2B",
		"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-2B" \
        --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-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zjr2000/SPES-2B with Docker Model Runner:
```
docker model run hf.co/zjr2000/SPES-2B
```

Add library_name and improve model card metadata

by nielsr HF Staff - opened 21 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+24

-18

Files changed (1) hide show

README.md +24 -18

README.md CHANGED Viewed

@@ -1,5 +1,9 @@
 ---
 license: apache-2.0
 tags:
 - moe
 - mixture-of-experts
@@ -8,46 +12,48 @@ tags:
 - distributed-training
 - decentralized-training
 - sparse-sync
-language:
-- en
-pipeline_tag: text-generation
 ---
 # SPES-2B
-SPES-2B is a pretrained language model released as part of paper:
-**Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm**
 ## Model Details
 - **Model name:** SPES-2B
-- **Model type:** Causal language model
 - **Parameters:** 2B
-- **Framework:** SPES
 - **License:** Apache-2.0
 ## Project Links
-- **GitHub:** https://github.com/zjr2000/SPES
-- **Paper:** https://huggingface.co/papers/2602.11543
 ## Intended Use
 This model is intended for:
-- research on decentralized LLM pretraining
-- research on MoE training and synchronization
-- experimentation and evaluation of pretrained language models
 ## Citation
-If you use this model, please cite the SPES paper.
 ```bibtex
-@article{zhang2026spes,
   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},
   year={2026}
-}

 ---
+language:
+- en
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 tags:
 - moe
 - mixture-of-experts
 - distributed-training
 - decentralized-training
 - sparse-sync
 ---
 # SPES-2B
+SPES-2B is a 2B-parameter Mixture-of-Experts (MoE) pretrained language model introduced in the paper:
+**[Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm](https://huggingface.co/papers/2602.11543)**
 ## Model Details
 - **Model name:** SPES-2B
+- **Model type:** Causal language model (MoE)
+- **Architecture:** OLMoE
 - **Parameters:** 2B
+- **Framework:** SPES (SParse Expert Synchronization)
 - **License:** Apache-2.0
+## Description
+SPES-2B was trained using **SPES**, a memory-efficient decentralized framework. Unlike traditional centralized training that requires high-bandwidth interconnects, SPES enables pretraining across geographically distributed GPU nodes by training only a subset of experts per node and periodically synchronizing them. This model was trained using 16 standalone 48GB GPUs over standard internet connections.
 ## Project Links
+- **GitHub:** [https://github.com/zjr2000/SPES](https://github.com/zjr2000/SPES)
+- **Paper:** [https://huggingface.co/papers/2602.11543](https://huggingface.co/papers/2602.11543)
 ## Intended Use
 This model is intended for:
+- Research on decentralized LLM pretraining.
+- Research on Mixture-of-Experts (MoE) training and synchronization.
+- Experimentation and evaluation of pretrained language models.
 ## Citation
+If you use this model, please cite the SPES paper:
 ```bibtex
+@article{zhang2026pretraining,
   title={Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm},
+  author={Zhang, Jinrui icon and Xiao, Chaodong and Wu, Aoqi and Zhang, Xindong and Zhang, Lei},
+  journal={arXiv preprint arXiv:2602.11543},
   year={2026}
+}
+```