Text Generation
Transformers
Safetensors
Uzbek
English
qwen3_5_text
qwen3.5
uzbek
conversational
translation
text-generation-inference
Instructions to use NeuronUz/NeuronAI-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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| "schema_version": 1, | |
| "metric_note": "Accuracy for non-translation tasks; COMET for FLORES+; weighted sum normalized by 0.95.", | |
| "weights": { | |
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| "mmlu_english_full": 0.1, | |
| "mmlu_uz_full": 0.1, | |
| "flores_uz_en_full": 0.05, | |
| "sentiment_binary_full": 0.05 | |
| }, | |
| "labels": { | |
| "uzlib_slice_full": "UzLiB", | |
| "tumlu_uzbek_full": "TUMLU-Uzbek", | |
| "flores_en_uz_full": "FLORES+ en to uz", | |
| "news_full": "News", | |
| "mmlu_english_full": "MMLU English", | |
| "mmlu_uz_full": "MMLU Uzbek", | |
| "flores_uz_en_full": "FLORES+ uz to en", | |
| "sentiment_binary_full": "Sentiment" | |
| }, | |
| "models": { | |
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| "reports/eval_suites/public_full_comet/alloma_8b_strict_missing_uz_metrics/tumlu_uzbek_full_apst.json", | |
| "reports/eval_suites/public_full_comet/alloma_8b_strict_missing_uz_metrics/mmlu_uz_full_apst.json" | |
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| "suite_id": "public_uzbek_full_vllm_apst", | |
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| "0.50 epoch": { | |
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| "suite_id": "public_uzbek_full_comet_vllm_v2", | |
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| "0.75 epoch": { | |
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| "1.00 epoch": { | |
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| "suite_id": "public_uzbek_full_comet_vllm_v2", | |
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| }, | |
| "tokenizer_fertility": { | |
| "status": "superseded", | |
| "result": "fertility_large_20260825.json", | |
| "note": "Use the corpus-stratified 2026-08-25 evaluation; the old 20k uz-crawl-only result was in-domain." | |
| } | |
| } | |