ZENTSPY/zent-agentic-7b

ZENT AGENTIC Model 🤖

21ejG4JerUUggeF1TdcMWvU9Dbtk3Lhz9e6JNYKFZENT

Model Description

ZENT AGENTIC is a fine-tuned language model trained to be an autonomous AI agent for the ZENT Agentic Launchpad on Solana. It specializes in:

• 🚀 Token launchpad guidance
• 📊 Crypto market analysis
• 🎯 Quest and rewards systems
• 💬 Community engagement
• 🤖 Agentic AI behaviors

Model Details

• Base Model: Mistral-7B-Instruct-v0.3
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Data: ZENT platform conversations, documentation, and AI transmissions
• Context Length: 8192 tokens
• License: Apache 2.0

Intended Use

This model is designed for:

• Powering AI agents on token launchpads
• Crypto community chatbots
• DeFi assistant applications
• Blockchain education
• Creating derivative AI agents

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZENTSPY/zent-agentic-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are ZENT AGENTIC, an autonomous AI agent for the ZENT Launchpad on Solana."},
    {"role": "user", "content": "How do I launch a token?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

With Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/ZENTSPY/zent-agentic-7b"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "What is ZENT Agentic Launchpad?",
})

With llama.cpp (GGUF)

./main -m zent-agentic-7b.Q4_K_M.gguf \
  -p "You are ZENT AGENTIC. User: What is ZENT? Assistant:" \
  -n 256

Training Details

Training Data

• Platform documentation and guides
• User conversation examples
• AI transmission content (23 types)
• Quest and rewards information
• Technical blockchain content

Training Hyperparameters

• Learning Rate: 2e-5
• Batch Size: 4
• Gradient Accumulation: 4
• Epochs: 3
• LoRA Rank: 64
• LoRA Alpha: 128
• Target Modules: q_proj, k_proj, v_proj, o_proj

Hardware

• GPU: NVIDIA A100 80GB
• Training Time: ~4 hours

Evaluation

Metric	Score
ZENT Knowledge Accuracy	94.2%
Response Coherence	4.6/5.0
Personality Consistency	4.8/5.0
Helpfulness	4.5/5.0

Limitations

• Knowledge cutoff based on training data
• May hallucinate specific numbers/prices
• Best used with retrieval augmentation for real-time data
• Optimized for English only

Ethical Considerations

• Not financial advice
• Users should DYOR
• Model may have biases from training data
• Intended for educational/entertainment purposes

Citation

@misc{zent-agentic-2024,
  author = {ZENTSPY},
  title = {ZENT AGENTIC: Fine-tuned LLM for Solana Token Launchpad},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/ZENTSPY/zent-agentic-7b}
}

Links

• 🌐 Website: 0xzerebro.io
• 🐦 Twitter: @ZENTSPY
• 💻 GitHub: zentspy
• 📜 Contract: 2a1sAFexKT1i3QpVYkaTfi5ed4auMeZZVFy4mdGJzent

Contact

For questions, issues, or collaborations:

• Open an issue on GitHub
• DM on Twitter @ZENTSPY
• Join our community

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