Text Generation
Transformers
Safetensors
llama
math
combinatorics
permutations
algebraic-combinatorics
causal-lm
text-generation-inference
Instructions to use ACDRepo/PermuFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ACDRepo/PermuFormer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ACDRepo/PermuFormer")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ACDRepo/PermuFormer") model = AutoModelForCausalLM.from_pretrained("ACDRepo/PermuFormer") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ACDRepo/PermuFormer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ACDRepo/PermuFormer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ACDRepo/PermuFormer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ACDRepo/PermuFormer
- SGLang
How to use ACDRepo/PermuFormer with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ACDRepo/PermuFormer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ACDRepo/PermuFormer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ACDRepo/PermuFormer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ACDRepo/PermuFormer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ACDRepo/PermuFormer with Docker Model Runner:
docker model run hf.co/ACDRepo/PermuFormer
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README.md
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## Training Data
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PermuFormer was trained autoregressively on synthetic permutation examples generated with exact combinatorial algorithms. The paper describes a dataset of 39.8M instances, approximately 2.66B tokens, over the symmetric groups
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Training tasks cover three broad families:
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- **Translation between encodings:**
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- **Permutation statistics and properties:**
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- **Algebraic operations and comparisons:**
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Some targets include computational witnesses before the final answer, for example
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## Usage
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## Training Data
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PermuFormer was trained autoregressively on synthetic permutation examples generated with exact combinatorial algorithms. The paper describes a dataset of 39.8M instances, approximately 2.66B tokens, over the symmetric groups `S_2` through `S_11`.
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Training tasks cover three broad families:
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- **Translation between encodings:** One-line notation, cycle notation, reduced Coxeter expressions, RSK tableaux, inversion vectors, and Lehmer codes.
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- **Permutation statistics and properties:** Length, descents, fixed points, sign/parity, cycle type, RSK shape, pattern avoidance, longest increasing/decreasing subsequences, and related statistics.
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- **Algebraic operations and comparisons:** Product/composition, inverse, powers, conjugation, commutator, relative products, multiplication by simple transpositions, complement, reverse, descent tests, and Bruhat order.
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Some targets include computational witnesses before the final answer, for example inversions are listed before a computation of length. Similarly pattern witnesses are stated before an avoidance answer.
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## Usage
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