Text Generation
Transformers
PyTorch
English
tamelm
Conversational
Cot
Symbiotic
symbioticai
Conversation Al
math
physics
convergentintel
Instructions to use reaperdoesntknow/TameForCasualLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaperdoesntknow/TameForCasualLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/TameForCasualLM")# Load model directly from transformers import TAMELM model = TAMELM.from_pretrained("reaperdoesntknow/TameForCasualLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use reaperdoesntknow/TameForCasualLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reaperdoesntknow/TameForCasualLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/TameForCasualLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reaperdoesntknow/TameForCasualLM
- SGLang
How to use reaperdoesntknow/TameForCasualLM 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 "reaperdoesntknow/TameForCasualLM" \ --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": "reaperdoesntknow/TameForCasualLM", "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 "reaperdoesntknow/TameForCasualLM" \ --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": "reaperdoesntknow/TameForCasualLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reaperdoesntknow/TameForCasualLM with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/TameForCasualLM
| { | |
| "adaptive_vortex": true, | |
| "alpha": 0.31415, | |
| "architectures": [ | |
| "TAMELM" | |
| ], | |
| "base_vortex_strength": 10000.0, | |
| "beta": 0.2718, | |
| "bos_token_id": 1, | |
| "discrepancy_module": true, | |
| "dropout": 0.2, | |
| "dt": 0.1, | |
| "dtype": "float32", | |
| "embed_dim": 512, | |
| "energy_amplification": 10000.0, | |
| "eos_token_id": 2, | |
| "expert_dim": 128, | |
| "ff_dim": 768, | |
| "freq_scale": 1.0, | |
| "k_fast": 64, | |
| "k_fast_dt": 0.1, | |
| "k_fast_freq": 0.01, | |
| "k_mid": 32, | |
| "k_mid_dt": 0.02, | |
| "k_mid_freq": 0.002, | |
| "k_slow": 8, | |
| "k_slow_dt": 0.0005, | |
| "k_slow_freq": 5e-05, | |
| "lambda_discrepancy": 0.3, | |
| "lambda_quantum": 0.001, | |
| "lambda_routing_entropy": 0.01, | |
| "max_seq_len": 512, | |
| "model_type": "tamelm", | |
| "n_attn_heads": 8, | |
| "n_edge_types": 1, | |
| "n_nodes": 16, | |
| "n_scales": 3, | |
| "n_steps": 2, | |
| "num_experts": 16, | |
| "num_layers": 26, | |
| "pad_token_id": 3, | |
| "phase_dim": 64, | |
| "sparsity_ratio": 0.55, | |
| "theta_base": 10000.0, | |
| "tie_weights": true, | |
| "transformers_version": "4.56.1", | |
| "unk_token_id": 0, | |
| "use_learned_adj": true, | |
| "use_multiscale": true, | |
| "vocab_path": "/content/vocab.txt", | |
| "vocab_size": 22468 | |
| } | |