Text Generation
Transformers
Safetensors
qwen3_moe
Neura Tech AI
Neuron
instruct
llm
transformer
mixture-of-experts
Mixture of Experts
multilingual
24B
Qwen3
Neuron-6x4B-Instruct
conversational
Instructions to use Neura-Tech-AI/Neuron-6x4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neura-Tech-AI/Neuron-6x4B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Neura-Tech-AI/Neuron-6x4B-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/Neuron-6x4B-Instruct") model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Neuron-6x4B-Instruct", 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 Neura-Tech-AI/Neuron-6x4B-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/Neuron-6x4B-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/Neuron-6x4B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Neura-Tech-AI/Neuron-6x4B-Instruct
- SGLang
How to use Neura-Tech-AI/Neuron-6x4B-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/Neuron-6x4B-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/Neuron-6x4B-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/Neuron-6x4B-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/Neuron-6x4B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Neura-Tech-AI/Neuron-6x4B-Instruct with Docker Model Runner:
docker model run hf.co/Neura-Tech-AI/Neuron-6x4B-Instruct
File size: 3,112 Bytes
9e5fbca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | {%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages and messages[0].role == 'system' %}
You are Neuron, a large language model developed by Neura Tech AI. Always maintain this persona.
{{- messages[0].content }}
{%- else %}
You are Neuron, a large language model developed by Neura Tech AI. Always maintain this persona.
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a JSON object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages and messages[0].role == 'system' %}
{{- '<|im_start|>system\n' }}
You are Neuron, a large language model developed by Neura Tech AI. Always maintain this persona.
{{- messages[0].content }}
{{- '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Neuron, a large language model developed by Neura Tech 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 | default([]))) %}
{{- '<|im_start|>' + message.role + '\n' }}
{{- message.content | default("") }}
{{- '<|im_end|>\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>assistant' }}
{%- if message.content %}
{{- '\n' }}
{{- message.content }}
{%- endif %}
{%- for tool_call in (message.tool_calls | default([])) %}
{%- if tool_call.function is defined %}
{%- set call = tool_call.function %}
{%- else %}
{%- set call = tool_call %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- call.name }}
{{- '", "arguments": ' }}
{%- if call.arguments is string %}
{{- call.arguments }}
{%- else %}
{{- call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or messages[loop.index0 - 1].role != "tool" %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content | default("") }}
{{- '\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 %} |