Text Generation
Transformers
Safetensors
PyTorch
English
yatnmn_gpt
gpt
yatnmn
nmn
chinchilla
nanochat
ablation
custom_code
Instructions to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch
- SGLang
How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch 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 "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch" \ --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": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch", "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 "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch" \ --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": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch with Docker Model Runner:
docker model run hf.co/mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-pytorch
| """HuggingFace-compatible config for the flaxchat YatNMN-Softplus GPT.""" | |
| from transformers import PretrainedConfig | |
| class YatGPTHfConfig(PretrainedConfig): | |
| model_type = "yatnmn_gpt" | |
| def __init__( | |
| self, | |
| sequence_len: int = 1024, | |
| vocab_size: int = 32768, | |
| n_layer: int = 12, | |
| n_head: int = 12, | |
| n_kv_head: int = 12, | |
| n_embd: int = 768, | |
| window_pattern: str = "SSSL", | |
| tie_embeddings: bool = True, | |
| rope_base: float = 100000.0, | |
| pad_vocab_size_to: int = 64, | |
| mlp_type: str = "yatnmn-softplus", | |
| scalar_bias: bool = False, | |
| softplus_bias: bool = True, | |
| learnable_epsilon: bool = True, | |
| epsilon_init: float = 1e-3, | |
| constant_alpha: bool = False, | |
| tie_word_embeddings: bool = True, | |
| **kwargs, | |
| ): | |
| self.sequence_len = sequence_len | |
| self.vocab_size = vocab_size | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_kv_head = n_kv_head | |
| self.n_embd = n_embd | |
| self.window_pattern = window_pattern | |
| self.tie_embeddings = tie_embeddings | |
| self.rope_base = rope_base | |
| self.pad_vocab_size_to = pad_vocab_size_to | |
| self.mlp_type = mlp_type | |
| self.scalar_bias = scalar_bias | |
| self.softplus_bias = softplus_bias | |
| self.learnable_epsilon = learnable_epsilon | |
| self.epsilon_init = epsilon_init | |
| self.constant_alpha = constant_alpha | |
| self.max_position_embeddings = sequence_len * 10 | |
| # HF probes these standard names | |
| self.num_hidden_layers = n_layer | |
| self.num_attention_heads = n_head | |
| self.num_key_value_heads = n_kv_head | |
| self.hidden_size = n_embd | |
| super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) | |