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
| { | |
| "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" | |
| ] | |
| } |