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
Download config.json from techdotus/Ethoric-10M: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/techdotus/Ethoric-10M/resolve/main/config.json
- Command line
-
hf download hf://techdotus/Ethoric-10M/config.json
-
curl -L -o config.json https://huggingface.co/techdotus/Ethoric-10M/resolve/main/config.json
1.07 kB
| { | |
| "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 | |
| } | |