Instructions to use SupraLabs/Supra2.1-Ultra-Preview-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra2.1-Ultra-Preview-001 with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra2.1-Ultra-Preview-001")
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("SupraLabs/Supra2.1-Ultra-Preview-001")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra2.1-Ultra-Preview-001", 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 SupraLabs/Supra2.1-Ultra-Preview-001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra2.1-Ultra-Preview-001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra2.1-Ultra-Preview-001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra2.1-Ultra-Preview-001

SGLang

How to use SupraLabs/Supra2.1-Ultra-Preview-001 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 "SupraLabs/Supra2.1-Ultra-Preview-001" \
    --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": "SupraLabs/Supra2.1-Ultra-Preview-001",
		"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 "SupraLabs/Supra2.1-Ultra-Preview-001" \
        --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": "SupraLabs/Supra2.1-Ultra-Preview-001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SupraLabs/Supra2.1-Ultra-Preview-001 with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra2.1-Ultra-Preview-001
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Supra2.1-Ultra

Supra2.1-Ultra is the model that refers to the Supra2.1 family. It has 230M params, 40B tokens trained on, with LLaMa arch. It is a small base model that is probably going to generate incorrect or biased text, and it has neither been aligned nor evaluated for safety.

Config

Spec	Details
Parameters	230M
Tokens seen	~40.4B
Arch	Llama
Layers	32
Hidden size	768
Intermediate size (MLP)	1920
Attention heads	12 (head dim 64)
KV heads (GQA)	4
Vocabulary size	49,152
Context length	2,048
RoPE theta	10,000
Activation	SiLU (SwiGLU MLP)
Tied embeddings	Yes
Precision	bfloat16

Data used to train this model

HuggingFaceFW/fineweb-edu

mlfoundations/dclm-baseline-1.0

HuggingFaceTB/finemath

Hardware

This model was trained on a single A100 SXM.

Benchmarks

Benchmark	Shots	acc	acc_norm	Random
HellaSwag	0	31.64	37.18	25.0
PIQA	0	65.78	64.91	50.0
ARC-Easy	0	57.11	50.46	25.0
ARC-Challenge	0	25.00	26.28	25.0
Arithmark-v3	0	38.00	38.20	-
Open SLM leaderboard's II	-	20.64	-	-

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SupraLabs/Supra2.1-Ultra"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)

prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt")

output = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

This code should download the model and generate a response.

License

This model is released under Apache-2.0 License.

Credits

thanks to:

Dedeprogames, for code to train this model.

Downloads last month: 399

Safetensors

Model size

0.2B params

Tensor type

BF16

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Quantizations

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