---
license: apache-2.0
base_model: qwen3
tags:
- affine
- qwen3
- causal-lm
- reasoning
library_name: transformers
pipeline_tag: text-generation
---

# Model Card

## Description

A Qwen3-based language model (~7B parameters) optimized for the Affine network. Features a 40K token context window, 36 transformer layers, and efficient grouped query attention (GQA) architecture. Designed for high-performance reasoning, code generation, and agentic AI applications.

## What is this used for?

- **Complex Reasoning**: Multi-step problem solving and logical deduction
- **Code Generation**: Python, JavaScript, and other programming languages  
- **Agentic AI**: Tool-using agents and autonomous systems
- **Long-Context Tasks**: Document analysis and research
- **Affine Network**: Competitive reasoning model for decentralized evaluation

## Quick Start

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

model_name = "your-username/your-model-name"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Explain quantum computing."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Model Details

- **Architecture**: Qwen3ForCausalLM
- **Parameters**: ~7B
- **Context Length**: 40,960 tokens
- **Layers**: 36
- **Precision**: bfloat16

## License

Apache 2.0