Instructions to use Neura-Tech-AI/Neuron-V1-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Neura-Tech-AI/Neuron-V1-14B-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-V1-14B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Neura-Tech-AI/Neuron-V1-14B-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]:]))

llama-cpp-python

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Neura-Tech-AI/Neuron-V1-14B-Instruct",
	filename="Neuron-Distill-Qwen2-14B-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
# Run inference directly in the terminal:
llama cli -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
# Run inference directly in the terminal:
llama cli -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Use Docker

docker model run hf.co/Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

LM Studio
Jan

vLLM

How to use Neura-Tech-AI/Neuron-V1-14B-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-V1-14B-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-V1-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

SGLang

How to use Neura-Tech-AI/Neuron-V1-14B-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-V1-14B-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-V1-14B-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-V1-14B-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-V1-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Ollama:
```
ollama run hf.co/Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
```

Unsloth Studio

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Neura-Tech-AI/Neuron-V1-14B-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Neura-Tech-AI/Neuron-V1-14B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Neura-Tech-AI/Neuron-V1-14B-Instruct to start chatting

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Docker Model Runner:
```
docker model run hf.co/Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K
```

Lemonade

How to use Neura-Tech-AI/Neuron-V1-14B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K

Run and chat with the model

lemonade run user.Neuron-V1-14B-Instruct-Q6_K

List all available models

lemonade list

🧠 Neura-Tech-AI/Neuron-Distill-Qwen2-14B: The Official Intelligence of Neura Tech AI

Neura-Tech-AI/Neuron-V1-14B-Instruct is a high-performance Large Language Model (LLM) developed by Neura Tech AI. It serves as the flagship model for advanced reasoning, creative synthesis, and multilingual communication.

🏢 Organization Identity

Company: Neura Tech AI
Project Name: Neuron
Lead Architect: Samarth Anand Pathak

📊 Model Specifications

Architecture: Optimized Transformer (Fine-tuned from Qwen2)
Parameters: ~15 Billion
Precision: BF16 (Bfloat16)
Context Window: 32K tokens
License: Apache-2.0 (Open Source)

🎯 Core Capabilities

Advanced Reasoning: Capable of solving complex logical and mathematical queries.
Multilingual Proficiency: Highly optimized for English and Hindi (including Hinglish).
Instruction Following: Specifically tuned to follow complex user prompts with high precision.
Creative Synthesis: Exceptional at generating scripts, stories, and technical documentation.

📜 License & Usage

This model is licensed under the Apache-2.0 License. This means you are free to use, modify, and distribute this model, provided that you credit Neura Tech as the original creator.

🛠️ Quick Start (Python)

To use Neura-Tech-AI/Neuron-V1-14B-Instruct, load it via the Hugging Face transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Neura-Tech-AI/Neuron-V1-14B-Instruct"

# Load Neuron-14B Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load Model Weights
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

Downloads last month: 231

Safetensors

Model size

15B params

Tensor type

F16

Model tree for Neura-Tech-AI/Neuron-V1-14B-Instruct

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Quantized

(141)

this model

Quantizations

7 models

Neura-Tech-AI
/

Neuron-V1-14B-Instruct