Instructions to use sKT-Ai-Labs/SPEED-1-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sKT-Ai-Labs/SPEED-1-BASE with Transformers:

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

pipe = pipeline("text-generation", model="sKT-Ai-Labs/SPEED-1-BASE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sKT-Ai-Labs/SPEED-1-BASE")
model = AutoModelForCausalLM.from_pretrained("sKT-Ai-Labs/SPEED-1-BASE", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sKT-Ai-Labs/SPEED-1-BASE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sKT-Ai-Labs/SPEED-1-BASE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sKT-Ai-Labs/SPEED-1-BASE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sKT-Ai-Labs/SPEED-1-BASE

SGLang

How to use sKT-Ai-Labs/SPEED-1-BASE 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 "sKT-Ai-Labs/SPEED-1-BASE" \
    --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": "sKT-Ai-Labs/SPEED-1-BASE",
		"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 "sKT-Ai-Labs/SPEED-1-BASE" \
        --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": "sKT-Ai-Labs/SPEED-1-BASE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sKT-Ai-Labs/SPEED-1-BASE with Docker Model Runner:
```
docker model run hf.co/sKT-Ai-Labs/SPEED-1-BASE
```

SPEED-1-BASE

File size: 1,252 Bytes

---
license: mit
tags:
- mixture-of-experts
- reasoning
- speed
pipeline_tag: text-generation
language:
- en
new_version: sKT-Ai-Labs/SPEED-1
library_name: transformers
---

### SPEED 1-BASE

<div align="center" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 1000px; margin: 0 auto; padding: 20px;">

  <div style="margin-bottom: 15px;">
    <a href="https://sktailabs.in">
      <img src="https://www.sktailabs.in/skt-icon-96.png" alt="SKT AI LABS Logo" style="width: 96px; height: 96px; object-fit: contain;" />
    </a>
  </div>
  <a href="https://sktailabs.in" style="text-decoration: none;">
    <h1 style="color:#1b4fe8; font-size:48px; font-weight:900; text-shadow: 2px 2px 6px rgba(0,0,0,0.2); cursor: pointer; margin: 0 0 15px 0;">SKT AI LABS</h1>
  </a>
</div>

![SPEED](https://cdn-uploads.huggingface.co/production/uploads/6970b3cd48a639796bf613ac/AMgzYhdGjuBJLGBZqg6G9.png)

**SPEED-1** is a Mixture-of-Experts (MoE) language model with ~19B parameters, built by SKT AI LABS Private Limited, an Indian AI company based in Sidhi, Madhya Pradesh. It is designed for complex tasks across a wide range, trained for complex reasoning, with chain-of-thought data spanning agentic tasks, mathematics, and coding.