Text Generation
MLX
Safetensors
Hungarian
llama
hungarian
emese
from-scratch
base-model
apple-silicon
Instructions to use emese-tech/er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use emese-tech/er with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("emese-tech/er") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use emese-tech/er with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "emese-tech/er" --prompt "Once upon a time"
- Atomic Chat
Download config.json from emese-tech/er: direct link, hf CLI and curl.
- Browser
- Download file 392 Bytes
-
https://huggingface.co/emese-tech/er/resolve/main/config.json
- Command line
-
hf download hf://emese-tech/er/config.json
-
curl -L -o config.json https://huggingface.co/emese-tech/er/resolve/main/config.json
392 Bytes
| { | |
| "model_type": "llama", | |
| "vocab_size": 32000, | |
| "d_model": 1280, | |
| "n_layers": 24, | |
| "n_heads": 20, | |
| "d_ff": 3456, | |
| "max_seq_len": 8192, | |
| "rope_theta": 10000.0, | |
| "rope_yarn": true, | |
| "rope_yarn_scale": 4.0, | |
| "rms_norm_eps": 1e-6, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "repetition_penalty": 1.2, | |
| "bos_token_id": 2, | |
| "eos_token_id": 3, | |
| "pad_token_id": 1 | |
| } | |