joehoover / zephyr-7b-alpha
A high-performing language model trained to act as a helpful assistant
Prediction
joehoover/zephyr-7b-alpha:14ec63365a1141134c41b652fe798633f48b1fd28b356725c4d8842a0ac151eeIDvcxozelbrhlz7bzmukjromel7aStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- debug
- top_k
- 50
- top_p
- 0.9
- prompt
- Can you help me answer a question?</s> <|assistant|> I'd be happy to help you answer any question you have. Please provide me with the question you'd like assistance with, and I'll do my best to provide you with an answer.</s> <|user|> Please help understand this riddle: "I’m tall when I’m young, and I’m short when I’m old. What am I?"
- temperature
- 0.75
- return_logits
- system_prompt
- You are a helpful assistant.
- max_new_tokens
- 128
- min_new_tokens
- -1
Prediction
joehoover/zephyr-7b-alpha:14ec63365a1141134c41b652fe798633f48b1fd28b356725c4d8842a0ac151eeIDd4vhk3tbsywlhylzl53kbnvjvuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- debug
- top_k
- 50
- top_p
- 0.9
- prompt
- Could you briefly explain how self attention works?
- temperature
- 0.75
- return_logits
- system_prompt
- You are a machine learning professor who is famous for simple, intuitive explanations.
- max_new_tokens
- 500
- min_new_tokens
- -1
Output
Sure! Self-attention is a mechanism in neural networks that allows the model to focus on specific parts of the input data that are relevant for the task at hand. Here's a brief overview: 1. The input data is split into smaller pieces, called "tokens" or "words". 2. Each token is represented as a vector, which can be thought of as a "feature vector" that encodes information about that token. 3. The model computes a "query" vector and a "key" vector for each token. 4. The model then calculates a "score" for each pair of query and key vectors. 5. The model then "weights" the scores based on how closely the query matches the key. 6. The model then calculates a "context" vector for each token by summing the weighted scores from all other tokens. 7. The context vector for each token provides information about how important that token is for the overall task, and can be used to generate an output or to feed into another layer of the model. Self-attention is a powerful tool for tasks that involve understanding complex relationships between different parts of the input data, such as machine translation or document classification.Generated in
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