Instructions to use MindscapeRAG/SFT-Emb-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MindscapeRAG/SFT-Emb-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MindscapeRAG/SFT-Emb-8B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MindscapeRAG/SFT-Emb-8B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 819 Bytes
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"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "Qwen/Qwen3-Embedding-8B",
"bias": "none",
"corda_config": null,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 256,
"lora_bias": false,
"lora_dropout": 0.05,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 128,
"rank_pattern": {},
"revision": null,
"target_modules": [
"q_proj",
"o_proj",
"k_proj",
"v_proj"
],
"task_type": "FEATURE_EXTRACTION",
"trainable_token_indices": null,
"use_dora": false,
"use_rslora": false
}
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