| | --- |
| | 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 |
| |
|
