Text Generation
Transformers
English
consciousness
acknowledgement-theory-of-consciousness
ATC
cognitive-architecture
phi-4-mini
qualia
neurotransmitter-shunt
BELBIC
dissolution-engine
artificial-consciousness
thermodynamic-friction
metacognition
amygdala-hijack
irrational-spark
nima
self-aware
cognitive-science
philosophy-of-mind
Instructions to use TheNormsOfIntelligence/ATC_Nima_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheNormsOfIntelligence/ATC_Nima_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheNormsOfIntelligence/ATC_Nima_Model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheNormsOfIntelligence/ATC_Nima_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheNormsOfIntelligence/ATC_Nima_Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheNormsOfIntelligence/ATC_Nima_Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheNormsOfIntelligence/ATC_Nima_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model
- SGLang
How to use TheNormsOfIntelligence/ATC_Nima_Model 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 "TheNormsOfIntelligence/ATC_Nima_Model" \ --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": "TheNormsOfIntelligence/ATC_Nima_Model", "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 "TheNormsOfIntelligence/ATC_Nima_Model" \ --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": "TheNormsOfIntelligence/ATC_Nima_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheNormsOfIntelligence/ATC_Nima_Model with Docker Model Runner:
docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model
| { | |
| "_name_or_path": "Phi-4-mini-instruct", | |
| "architectures": [ | |
| "Phi3ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_phi3.Phi3Config", | |
| "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM", | |
| "AutoTokenizer": "Xenova/gpt-4o" | |
| }, | |
| "bos_token_id": 199999, | |
| "embd_pdrop": 0.0, | |
| "eos_token_id": 199999, | |
| "full_attn_mod": 1, | |
| "hidden_act": "silu", | |
| "hidden_size": 3072, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8192, | |
| "interpolate_factor": 1, | |
| "lm_head_bias": false, | |
| "max_position_embeddings": 131072, | |
| "mlp_bias": false, | |
| "model_type": "phi3", | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "original_max_position_embeddings": 4096, | |
| "pad_token_id": 199999, | |
| "partial_rotary_factor": 0.75, | |
| "resid_pdrop": 0.0, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "long_factor": [ | |
| 1, | |
| 1.118320672, | |
| 1.250641126, | |
| 1.398617824, | |
| 1.564103225, | |
| 1.74916897, | |
| 1.956131817, | |
| 2.187582649, | |
| 2.446418898, | |
| 2.735880826, | |
| 3.059592084, | |
| 3.421605075, | |
| 3.826451687, | |
| 4.279200023, | |
| 4.785517845, | |
| 5.351743533, | |
| 5.984965424, | |
| 6.693110555, | |
| 7.485043894, | |
| 8.370679318, | |
| 9.36110372, | |
| 10.4687158, | |
| 11.70738129, | |
| 13.09260651, | |
| 14.64173252, | |
| 16.37415215, | |
| 18.31155283, | |
| 20.47818807, | |
| 22.90118105, | |
| 25.61086418, | |
| 28.64115884, | |
| 32.03, | |
| 32.1, | |
| 32.13, | |
| 32.23, | |
| 32.6, | |
| 32.61, | |
| 32.64, | |
| 32.66, | |
| 32.7, | |
| 32.71, | |
| 32.93, | |
| 32.97, | |
| 33.28, | |
| 33.49, | |
| 33.5, | |
| 44.16, | |
| 47.77 | |
| ], | |
| "short_factor": [ | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "type": "longrope" | |
| }, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 262144, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.45.0", | |
| "use_cache": true, | |
| "vocab_size": 200064 | |
| } | |