Feature Extraction
Transformers
Safetensors
sentence-transformers
English
code
qwen2
text-generation
embeddings
retrieval
code-search
semantic-search
Eval Results (legacy)
text-embeddings-inference
Instructions to use faisalmumtaz/codecompass-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faisalmumtaz/codecompass-embed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="faisalmumtaz/codecompass-embed")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("faisalmumtaz/codecompass-embed") model = AutoModelForCausalLM.from_pretrained("faisalmumtaz/codecompass-embed") - sentence-transformers
How to use faisalmumtaz/codecompass-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("faisalmumtaz/codecompass-embed") 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] - Notebooks
- Google Colab
- Kaggle
Fix: model.layers instead of model.model.layers (validated working)
Browse files
README.md
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@@ -130,7 +130,7 @@ model = AutoModel.from_pretrained("faisalmumtaz/codecompass-embed", trust_remote
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tokenizer = AutoTokenizer.from_pretrained("faisalmumtaz/codecompass-embed")
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# CRITICAL: Enable bidirectional attention for embeddings
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for layer in model.
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layer.self_attn.is_causal = False
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained("faisalmumtaz/codecompass-embed")
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# CRITICAL: Enable bidirectional attention for embeddings
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for layer in model.layers:
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layer.self_attn.is_causal = False
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model.eval()
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