README.md

---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- zent
- solana
- defi
- ai-agent
- crypto
- launchpad
- fine-tuned
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- ZENTSPY/zent-conversations
---

# ZENT AGENTIC Model 🤖

<img src="./zent.png" alt="ZENT Logo" width="200"/>
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

```python
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

```python
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)

```bash
./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

```bibtex
@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](https://0xzerebro.io)
- 🐦 Twitter: [@ZENTSPY](https://x.com/ZENTSPY)
- 💻 GitHub: [zentspy](https://github.com/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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