Instructions to use Aryagm/HRM-Text-1B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Aryagm/HRM-Text-1B-MLX-4bit 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("Aryagm/HRM-Text-1B-MLX-4bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use Aryagm/HRM-Text-1B-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Aryagm/HRM-Text-1B-MLX-4bit" --prompt "Once upon a time"
File size: 871 Bytes
3ef4009 | 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 | {
"model_type": "hrm_text",
"architectures": [
"HrmTextForCausalLM"
],
"vocab_size": 65536,
"hidden_size": 1536,
"intermediate_size": 4096,
"num_hidden_layers": 16,
"num_attention_heads": 12,
"num_key_value_heads": 12,
"head_dim": 128,
"H_cycles": 2,
"L_cycles": 3,
"L_bp_cycles": [
0,
3
],
"max_position_embeddings": 4096,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"initializer_range": 0.025515518153991442,
"embedding_scale": 39.191835884530846,
"prefix_lm": true,
"pad_token_id": 5,
"bos_token_id": 6,
"eos_token_id": 11,
"auto_map": {
"AutoConfig": "configuration_hrm_text.HrmTextConfig",
"AutoModel": "modeling_hrm_text.HrmTextModel",
"AutoModelForCausalLM": "modeling_hrm_text.HrmTextForCausalLM"
},
"mlx_format_version": 1,
"mlx_dtype": "bfloat16"
}
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