Instructions to use hf-internal-testing/tiny-muse-glimmer-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-muse-glimmer-assistant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-muse-glimmer-assistant")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-muse-glimmer-assistant") model = AutoModel.from_pretrained("hf-internal-testing/tiny-muse-glimmer-assistant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 778 Bytes
fa500a8 | 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 | {
"architectures": [
"MuseGlimmerAssistantModel"
],
"attention_dropout": 0,
"block_size": 4,
"bos_token_id": 2,
"dtype": "float32",
"eos_token_id": 3,
"head_dim": 8,
"hidden_act": "silu",
"hidden_size": 32,
"intermediate_size": 37,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention"
],
"mask_token_id": 1,
"max_position_embeddings": 131072,
"model_type": "muse_glimmer_assistant",
"num_attention_heads": 4,
"num_hidden_layers": 3,
"num_key_value_heads": 2,
"pad_token_id": 4,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 500000.0,
"rope_type": "default"
},
"sliding_window": 2048,
"target_layer_ids": [
0,
2
],
"transformers_version": "5.16.0.dev0"
}
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