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meta-llama/Llama-2-13b-hf

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. It was trained using a combination of publicly available online data and custom training libraries, and evaluated on several benchmarks. It achieves state-of-the-art results on many benchmarks, especially when fine-tuned on specific tasks. However, due to its risk of generating inappropriate content, users must take responsibility for testing and filtering output before deployment.

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. It was trained using a combination of publicly available online data and custom training libraries, and evaluated on several benchmarks. It achieves state-of-the-art results on many benchmarks, especially when fine-tuned on specific tasks. However, due to its risk of generating inappropriate content, users must take responsibility for testing and filtering output before deployment.

Public
$0.0005/sec

Input

text to generate from

maximum length of the newly generated generated text (Default: 2048, 1 ≤ max_new_tokens ≤ 100000)

Temperature

temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity (Default: 0.7, 0 ≤ temperature ≤ 100)

Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens (Default: 0.9, 0 < top_p ≤ 1)

Sample from the best k (number of) tokens. 0 means off (Default: 0, 0 ≤ top_k < 100000)

Repetition Penalty

repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition. (Default: 1, 0.01 ≤ repetition_penalty ≤ 5)

Up to 4 strings that will terminate generation immediately. Please separate items by comma

Num Responses

Number of output sequences to return. Incompatible with streaming (Default: 1, 1 ≤ num_responses ≤ 2)

Presence Penalty

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ presence_penalty ≤ 2)

Frequency Penalty

Positive values penalize new tokens based on how many times they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ frequency_penalty ≤ 2)

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Output

I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and

 


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