meta-llama/Llama-2-7b-hf
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Llama 2 models outperform open-source chat models on most benchmarks tested and are optimized for dialogue use cases. The model is intended for commercial and research use in English, and the pretrained models can be adapted for various natural language generation tasks.
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Llama 2 models outperform open-source chat models on most benchmarks tested and are optimized for dialogue use cases. The model is intended for commercial and research use in English, and the pretrained models can be adapted for various natural language generation tasks.
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: 1.2, 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)
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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
