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OpenChat 3.5 7B

openchat/openchat-7b:free

Model weights

OpenChat 7B is a library of open-source language models, fine-tuned with "C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.

  • For OpenChat fine-tuned on Mistral 7B, check out OpenChat 7B.
  • For OpenChat fine-tuned on Llama 8B, check out OpenChat 8B.

#open-source

Modalities
Price
Free
Context
8K
Released
Nov 28, 2023
Knowledge Cutoff
Sep 2023
ActivityFAQExplore

Activity

Token volume and request traffic to this model over time.

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Frequently asked questions

OpenChat 7B is a library of open-source language models, fine-tuned with "C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels. - For OpenChat fine-tuned on Mistral 7B, check out OpenChat 7B. - For OpenChat fine-tuned on Llama 8B, check out OpenChat 8B. #open-source

Yes. The pricing shown on this page for OpenChat 3.5 7B is zero, so you are not charged for prompt or completion tokens. Free endpoints are rate limited — see the rate limit docs.

OpenChat 3.5 7B has a 8,192 token context window.

OpenChat 3.5 7B was released on November 28, 2023. Its knowledge cutoff is September 30, 2023.

More models from OpenChat

OpenChat 3.6 8B

OpenChat 8B is a library of open-source language models, fine-tuned with "C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.

It outperforms many similarly sized models including Llama 3 8B Instruct and various fine-tuned models. It excels in general conversation, coding assistance, and mathematical reasoning.

#open-source

Text8K context
OpenChat 3.5 7B

OpenChat 7B is a library of open-source language models, fine-tuned with "C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.

#open-source

Text8K context