Text Generation
Transformers
Safetensors
English
ethoric
causal-lm
custom-architecture
custom_code
slm
small-language-model
Eval Results (legacy)
Instructions to use techdotus/Ethoric-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techdotus/Ethoric-10M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="techdotus/Ethoric-10M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("techdotus/Ethoric-10M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use techdotus/Ethoric-10M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "techdotus/Ethoric-10M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "techdotus/Ethoric-10M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/techdotus/Ethoric-10M
- SGLang
How to use techdotus/Ethoric-10M 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 "techdotus/Ethoric-10M" \ --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": "techdotus/Ethoric-10M", "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 "techdotus/Ethoric-10M" \ --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": "techdotus/Ethoric-10M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use techdotus/Ethoric-10M with Docker Model Runner:
docker model run hf.co/techdotus/Ethoric-10M
File size: 1,066 Bytes
429b898 d8c245b 429b898 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"architectures": [
"EthoricForCausalLM"
],
"attn_gate": "headwise",
"attn_window": 16,
"attn_window_layers": [
0
],
"auto_map": {
"AutoConfig": "configuration_ethoric.EthoricConfig",
"AutoModelForCausalLM": "modeling_ethoric.EthoricForCausalLM"
},
"bos_token_id": null,
"canon": {
"doc_masking": true,
"enabled": true,
"kernel": 4,
"positions": [
"A",
"C"
]
},
"d_model": 256,
"doc_masking": true,
"dtype": "float32",
"eos_token_id": 1,
"ffn_hidden": 721,
"head_dim": 32,
"hidden_size": 256,
"intermediate_size": 721,
"max_position_embeddings": 512,
"max_seq_len": 512,
"model_type": "ethoric",
"n_head": 8,
"n_kv_head": 2,
"n_layer": 12,
"n_special": 3,
"norm_eps": 1e-05,
"num_attention_heads": 8,
"num_hidden_layers": 12,
"num_key_value_heads": 2,
"pad_token_id": 0,
"peri_ln": true,
"qk_norm": "per_head",
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"tie_word_embeddings": true,
"transformers_version": "5.16.1",
"vocab_size": 5048
}
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