metadata
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
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