Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3_pseudo_moe
sentence-similarity
custom_code
Instructions to use geevec-ai/geevec-embeddings-1.0-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use geevec-ai/geevec-embeddings-1.0-lite with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("geevec-ai/geevec-embeddings-1.0-lite", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use geevec-ai/geevec-embeddings-1.0-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="geevec-ai/geevec-embeddings-1.0-lite", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("geevec-ai/geevec-embeddings-1.0-lite", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,457 Bytes
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"architectures": [
"Qwen3PseudoMoEModelModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "modeling_qwen3_pseudo_moe.Qwen3PseudoMoEConfig",
"AutoModel": "modeling_qwen3_pseudo_moe.Qwen3PseudoMoEModelModel"
},
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 12,
"model_type": "qwen3_pseudo_moe",
"num_attention_heads": 16,
"num_hidden_layers": 12,
"num_key_value_heads": 8,
"proj_dim": 4096,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.1",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936,
"lora_rank": 32,
"lora_alpha": 64.0,
"lora_target_modules": [
"o_proj",
"proj_linear",
"k_proj",
"v_proj",
"q_proj",
"up_proj",
"gate_proj",
"down_proj"
],
"domain_names": [
"coding",
"reasoning"
]
} |