| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - text-generation |
| - affine-subnet |
| - reasoning |
| - custom-trained |
| pipeline_tag: text-generation |
| --- |
| |
| # your-username/affine-custom-model-v1 |
|
|
| A custom-trained reasoning model optimized for the Affine subnet on Bittensor. |
|
|
| ## Model Details |
|
|
| - **Architecture**: GPT-OSS based transformer |
| - **Training**: Fine-tuned on multi-task reasoning datasets |
| - **Optimization**: Custom RL training for Affine environments |
| - **Modified**: 2025-11-13 |
|
|
| ## Environments |
|
|
| Optimized for: |
| - SAT solving |
| - Abductive reasoning (ABD) |
| - Deductive reasoning (DED) |
| - ALFWorld navigation |
| - WebShop interaction |
| - BabyAI tasks |
| - SciWorld experiments |
| - TextCraft games |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "your-username/affine-custom-model-v1", |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("your-username/affine-custom-model-v1") |
| |
| messages = [{"role": "user", "content": "Solve this SAT problem..."}] |
| inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") |
| outputs = model.generate(inputs, max_new_tokens=512) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| ## Performance |
|
|
| This model achieves competitive performance across all Affine evaluation environments. |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
