Text Generation
Transformers
PyTorch
English
burt-imma
custom-architecture
matrix-memory
equilibrium-propagation
cifg
sovereign
snapkitty
no-backprop
formal-verification
lean4
agents
python
Instructions to use Snapkitty/burt-imma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Snapkitty/burt-imma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Snapkitty/burt-imma")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Snapkitty/burt-imma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Snapkitty/burt-imma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Snapkitty/burt-imma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Snapkitty/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Snapkitty/burt-imma
- SGLang
How to use Snapkitty/burt-imma 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 "Snapkitty/burt-imma" \ --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": "Snapkitty/burt-imma", "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 "Snapkitty/burt-imma" \ --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": "Snapkitty/burt-imma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Snapkitty/burt-imma with Docker Model Runner:
docker model run hf.co/Snapkitty/burt-imma
Download config.json from Snapkitty/burt-imma: direct link, hf CLI and curl.
- Browser
- Download file 556 Bytes
-
https://huggingface.co/Snapkitty/burt-imma/resolve/main/config.json
- Command line
-
hf download hf://Snapkitty/burt-imma/config.json
-
curl -L -o config.json https://huggingface.co/Snapkitty/burt-imma/resolve/main/config.json
556 Bytes
| { | |
| "model_type": "burt-imma", | |
| "architectures": ["BurtImmaForCausalLM"], | |
| "num_layers": 13, | |
| "hidden_size": 1024, | |
| "matrix_memory_dim": 1024, | |
| "num_induction_heads": 8, | |
| "cifg_gate_dim": 1024, | |
| "entropy_cap": 0.20, | |
| "spectral_radius_bound": 1.0, | |
| "smooth_leaky_alpha": 0.01, | |
| "smooth_leaky_beta": 1.0, | |
| "mmep_beta": 0.1, | |
| "mmep_phases": 2, | |
| "vocab_size": 32000, | |
| "max_position_embeddings": 4096, | |
| "learning_rule": "mmep", | |
| "cuda_compute": "sm_86", | |
| "lean4_verified": true, | |
| "transformers_version": "4.40.0", | |
| "torch_dtype": "float32" | |
| } | |