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

Libraries

How to use NeuronUz/NeuronAI-2B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeuronUz/NeuronAI-2B")
model = AutoModelForCausalLM.from_pretrained("NeuronUz/NeuronAI-2B", 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 NeuronUz/NeuronAI-2B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NeuronUz/NeuronAI-2B

SGLang

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

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

NeuronAI-2B / fertility_large_20260825.md

kmamaroziqov

Match NeuronAI-4B card format with 2B evidence and Apache license

f99a82c verified 5 days ago

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	# NeuronAI-2B large tokenizer fertility evaluation

	Generated: 2026-08-24T19:25:09+00:00

	Fertility is the number of tokenizer tokens per whitespace-delimited word; lower is better.
	The confidence interval is a paired 5,000-replicate bootstrap over 1,000 deterministic document buckets.

	\| Corpus \| Domain status \| Documents \| Words \| NeuronAI-2B \| Qwen3.5-2B \| Token reduction (95% CI) \|
	\| --- \| --- \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| OSCAR Uzbek \| held-out web source \| 100,000 \| 7,618,770 \| 2.0304 \| 3.3639 \| 39.64% (39.57%–39.71%) \|
	\| Uzbek legal corpus \| held-out legal source/domain \| 18,832 \| 2,534,566 \| 2.3747 \| 2.9705 \| 20.06% (19.55%–20.57%) \|
	\| uz-crawl control \| training-source control \| 100,000 \| 20,825,680 \| 2.3206 \| 3.3224 \| 30.15% (30.02%–30.30%) \|

	## Interpretation

	- The 2B and 4B evaluated tokenizer pairs are byte-identical, so their token counts are exactly identical. SHA-256 fingerprints are recorded in the JSON result.
	- The held-out-source corpora are the fairness readout; the training-source corpus is retained only as a control.
	- Exact web overlap cannot be ruled out for OSCAR because both corpora ultimately derive from public web data.
	- OSCAR was checked in a post-hoc weak-token coverage analysis but contributed no tokenizer-training rows.
	- The legal corpus is absent from the tokenizer and CPT source manifests and is the cleanest source-and-domain holdout in this run.
	- Results use raw source text without apostrophe normalization or other model-specific preprocessing.