---
language:
- en
license: mit
tags:
- text-generation
- mistral
- business
- technology
- assistant
- conversational
- safetensors
- AI
- problem-solving
pipeline_tag: text-generation
inference: true
widget:
- text: How can I improve my business strategy?
---

# SireIQ - Professional AI Assistant

<div style="text-align: center;">
  <img src="https://huggingface.co/tarvico/sireiq/resolve/main/assets/sireiq-logo.png" alt="SireIQ Logo" style="width: 200px; margin-bottom: 20px;">
</div>

## 🌟 About SireIQ

SireIQ is a professional AI assistant designed specifically for business, technology, and problem-solving tasks. Built on Mistral-7B-Instruct-v0.3 and fine-tuned on over 25,000 high-quality instruction samples, SireIQ provides clear, accurate, and actionable advice tailored for professionals and teams.

## ✨ Key Features

- **Business Strategy**: Help with go-to-market strategies, business planning, and decision-making
- **Technology Guidance**: Explain complex technical concepts, coding assistance, and architecture recommendations
- **Problem-Solving**: Step-by-step solutions for professional challenges
- **Conversational**: Natural, professional dialogue with proper system prompt support
- **Lightweight**: 7B parameters, optimized for fast inference

## 🚀 Quick Start

### Using Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "tarvico/sireiq"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Define system prompt and user query
system_prompt = "You are SireIQ, a professional AI assistant for business, technology, and problem-solving. You provide clear, accurate, and actionable advice tailored for professionals and teams."
user_query = "What are the key steps to build a go-to-market strategy for a SaaS product?"

# Format the conversation
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_query}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

# Print the result
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Simple Inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("tarvico/sireiq")
model = AutoModelForCausalLM.from_pretrained(
    "tarvico/sireiq",
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Explain AI in simple terms."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_new_tokens=200)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
```

## 📋 Model Details

| Attribute | Value |
|-----------|-------|
| **Base Model** |  |
| **Parameters** | 7B |
| **Fine-tuning Method** | LoRA |
| **Training Samples** | 25,000+ |
| **License** | MIT |
| **Language** | English |

## 📊 Training Data

SireIQ was fine-tuned on a diverse dataset including:
- Databricks Dolly-15k
- OpenHermes-2.5 (business/tech filtered)
- WizardLM Evol-Instruct-70k
- Alpaca-cleaned
- GSM8K (math reasoning)

## 🔒 License

This model is released under the MIT License. See the [LICENSE](LICENSE) file for more details.

## 📞 Support

For questions or issues, please open a discussion on the [Hugging Face repo](https://huggingface.co/tarvico/sireiq/discussions).

## 🤝 Contributing

Contributions to improve SireIQ are welcome! Please feel free to submit issues or pull requests.