last_hidden_state

```python def retrieve(queries): # Tokenize the queries inputs = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") # Perform retrieval using the LLM outputs = model(**inputs…

query = "How do I optimize LLM retrieval latency?" results = retrieve(query) print(results) ``` ### 4. **Efficient Tokenization** - **Tokenization Settings**: Ensure that tokenization settings are optimized. For example, usi…

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") # Move the model to the GPU device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # Define a function to perform retrieval def retrieve(…

model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Define a function to generate embeddings def generate_embeddings(text): inputs = tokenizer(text, ret…

# Load pre-trained model and tokenizer model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Move the model to GPU if available device = torch.device("cuda" …

[2026-03-14 06:34] xenonfun: ``` 3. Manual attention (lines 110-128) — Hand-rolled softmax attention instead of using mx.fast.scaled_dot_product_attention. MLX's fused attention kernel is significantly faster for small sequence lengths. …

with torch.no_grad(): doc_outputs = model(**doc_inputs) query_outputs = model(**query_inputs) doc_embeddings = doc_outputs.last_hidden_state.mean(dim=1) query_embedding = query_outputs.last_hidden_state.mean(dim…

inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state[:, 0, :] return embeddings # Test the function texts = ['This is a test sentence…

- **Combine Multiple Methods**: Combine contextual word embeddings, knowledge graphs, and rule-based systems to leverage the strengths of each approach. ### Example Implementation Using Contextual Word Embeddings Here's an example of h…

# Average the embeddings of the term tokens if term_start is not None and term_end is not None: term_embedding = last_hidden_state[:, term_start:term_end, :].mean(dim=1) else: term_embedding = torch.zeros((1…

expanded_query = query for lang in languages: if lang != 'en': # Use translation API or model to expand query # For simplicity, we assume a translation function `translate` translated_quer…

- **Multilingual Embeddings**: Use pre-trained models like `BERT` or `mBert`. - **Cross-Lingual Indexing**: Implement indexing using embeddings. - **Query Expansion**: Use translation APIs to expand queries. - **Hybrid Ranking**: Co…

def add_token(self, token): self.tokens.append(token) self.token_count += 1 def get_context(self): if self.token_count in self.cache: return self.cache[self.token_count] context = list(s…

query_embeddings = model(**query_encodings)['last_hidden_state'][:, 0, :] passage_embeddings = model(**passage_encodings)['last_hidden_state'][:, 0, :] # Apply dropout query_embeddings = dropout(query_embedd…

- **Rate Limits**: Be aware of Jira's rate limits and ensure your script respects them. By following these steps and using the provided example, you should be able to effectively track your sprint progress using the Jira API. [Turn 8918] …

{'id': 1, 'text': 'This is a relevant result'}, {'id': 2, 'text': 'This is another relevant result'}, {'id': 3, 'text': 'This is an irrelevant result'} ] query = 'Find relevant results' ranked_results = rerank_search_results(s…

Here's how you can implement these optimizations: #### 1. Batch Processing Process multiple texts in a single batch to take advantage of parallel processing. #### 2. Model Quantization Use quantization to reduce the precision of the mod…

quantized_model.to(device) # Define a function to perform batch inference with the quantized model def perform_quantized_batch_inference(texts): # Tokenize the input texts inputs = tokenizer(texts, return_tensors="pt", padding=True…

return outputs.last_hidden_state[:, 0, :] # Test the inference function text = "This is a sample text" output = perform_inference(text) print(output) ``` ->-> 10,15 [Turn 9751] Assistant: To optimize the inference time for your Huggin…

inputs = {k: v.to(device) for k, v in inputs.items()} # Perform inference with torch.no_grad(): outputs = quantized_model(**inputs) # Return the output return outputs.last_hidden_state[:, 0, :] # Test the quanti…

inputs = tokenizer(term, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings ``` ### Step 4: Retrieve Synonyms B…

closest_synonyms.extend([synonyms[i] for i in np.argsort(similarities)[-2:]]) # Take top 2 closest synonyms return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonym…

### Step 3: Initialize Redis for Caching Initialize Redis to cache the contextual embeddings and synonyms: ```python import redis redis_client = redis.Redis(host='localhost', port=6379, db=0) ``` ### Step 4: Generate Contextual Embeddin…

- **Background Information**: Provide background information and rationale for the implementation. #### Priorities: - **Clear Documentation**: Ensure that the documentation is clear and comprehensive. - **User-Friendly**: Make the document…

Here's an example of a hybrid approach that combines WordNet and context-aware embeddings: ```python from transformers import BertTokenizer, BertModel import torch import nltk from nltk.corpus import wordnet nltk.download('wordnet') toke…

synonym_embedding = synonym_outputs.last_hidden_state[0][0] # [CLS] token embedding similarity = torch.dot(word_embedding, synonym_embedding).item() if similarity > best_similarity: best_similar…