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
```

kmamaroziqov commited on 6 days ago

Commit

65de582

verified ·

1 Parent(s): dc86321

Make training section concise

Browse files

Files changed (1) hide show

README.md +5 -23

README.md CHANGED Viewed

@@ -133,29 +133,11 @@ on FLORES+ en->uz (0.8762 against alloma-3B's 0.8673) and on TUMLU-Uzbek, which
 the weakest task for every model tested, ours included -- at 0.3257 it is close
 enough to the 0.25 four-way chance level to treat as unsolved at this scale.
-## Training summary
-- Base: Uzbek CPT + anneal checkpoint
-- Method: LoRA, merged for release
-- LoRA rank/alpha/dropout: 64 / 128 / 0.05
-- LoRA targets: all linear layers
-- Trainable adapter parameters: 67,276,800
-- Training data: 152,152 grouped and globally deduplicated examples
-- Validation data: 1,537 grouped examples
-- Train/dev prompt-group overlap: 0
-- Sequence length: 2,048
-- Packing: disabled
-- Epochs: 1
-- Effective batch size: 32
-- Learning rate: 1e-4, cosine schedule, 3% warmup
-- Optimizer: AdamW, beta2 0.95, weight decay 0.01
-- Precision: bf16 with selected recurrent parameters retained in fp32
-- Loss: assistant turns only; `<|im_end|>` explicitly supervised
-The training mixture is Uzbek-first and includes clean general assistant data,
-translation, Uzbek language and literature, spelling and orthography, MMLU-style
-tasks, classification, math, and English-retention examples. Training data is
-not distributed in this model repository.
 ## Usage

 the weakest task for every model tested, ours included -- at 0.3257 it is close
 enough to the 0.25 four-way chance level to treat as unsolved at this scale.
+## Training
+One epoch of assistant-only LoRA fine-tuning (rank 64, alpha 128) on 152,152
+Uzbek-first examples, using a 2,048-token context and effective batch size 32.
+The adapter was merged into the Uzbek CPT + anneal checkpoint for release.
 ## Usage