How to use from
OpenClaw
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 OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Neura-Tech-AI/Neuron-V1-14B-Instruct:Q6_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Quick Links

🧠 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"
)

© 2026 Neura Tech AI. All Rights Reserved.

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