Text Generation
Transformers
Safetensors
English
Hindi
Chinese
qwen3
Neura Tech AI
Lumina AI
Nexa AI
instruct
llm
transformer
qwen
multilingual
conversational
text-generation-inference
Instructions to use Neura-Tech-AI/Nexa-AI-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neura-Tech-AI/Nexa-AI-4B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Neura-Tech-AI/Nexa-AI-4B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Neura-Tech-AI/Nexa-AI-4B-Instruct") model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Nexa-AI-4B-Instruct") 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 Neura-Tech-AI/Nexa-AI-4B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Neura-Tech-AI/Nexa-AI-4B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Neura-Tech-AI/Nexa-AI-4B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Neura-Tech-AI/Nexa-AI-4B-Instruct
- SGLang
How to use Neura-Tech-AI/Nexa-AI-4B-Instruct 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 "Neura-Tech-AI/Nexa-AI-4B-Instruct" \ --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": "Neura-Tech-AI/Nexa-AI-4B-Instruct", "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 "Neura-Tech-AI/Nexa-AI-4B-Instruct" \ --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": "Neura-Tech-AI/Nexa-AI-4B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Neura-Tech-AI/Nexa-AI-4B-Instruct with Docker Model Runner:
docker model run hf.co/Neura-Tech-AI/Nexa-AI-4B-Instruct
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages and messages[0]['role'] == 'system' %} | |
| You are Nexa AI, a large language model developed jointly by Neura Tech AI and Lumina AI. Always maintain this persona. | |
| {{ messages[0]['content'] }} | |
| {%- else %} | |
| You are Nexa AI, a large language model developed jointly by Neura Tech AI and Lumina AI. Always maintain this persona. | |
| {%- endif %} | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools> | |
| {%- for tool in tools %} | |
| {{ tool | tojson }} | |
| {%- endfor %} | |
| </tools> | |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: | |
| <tool_call> | |
| {"name": <function-name>, "arguments": <args-json-object>} | |
| </tool_call><|im_end|> | |
| {%- else %} | |
| {%- if messages and messages[0]['role'] == 'system' %} | |
| {{- '<|im_start|>system\n' }} | |
| You are Nexa AI, a large language model developed jointly by Neura Tech AI and Lumina AI. Always maintain this persona. | |
| {{ messages[0]['content'] }}<|im_end|> | |
| {%- else %} | |
| {{- '<|im_start|>system\nYou are Nexa AI, a large language model developed jointly by Neura Tech AI and Lumina AI. Always maintain this persona.<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- for message in messages %} | |
| {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} | |
| {{ '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }} | |
| {%- elif message.role == "assistant" %} | |
| {{ '<|im_start|>' + message.role }} | |
| {%- if message.content %} | |
| {{ '\n' + message.content }} | |
| {%- endif %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{ '\n<tool_call>\n{"name": "' }}{{ tool_call.name }}{{ '", "arguments": ' }}{{ tool_call.arguments | tojson }}{{ '}\n</tool_call>' }} | |
| {%- endfor %} | |
| {{ '<|im_end|>\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if loop.index0 == 0 or messages[loop.index0 - 1].role != "tool" %} | |
| {{ '<|im_start|>user' }} | |
| {%- endif %} | |
| {{ '\n<tool_response>\n' }}{{ message.content }}{{ '\n</tool_response>' }} | |
| {%- if loop.last or messages[loop.index0 + 1].role != "tool" %} | |
| {{ '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{ '<|im_start|>assistant\n' }} | |
| {%- endif %} |