Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.
Designed for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.
See the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
Note: this is a free, rate-limited version of [Gemma 2 9B](/models/google/gemma-2-9b-it). Outputs may be cached. Read about rate limits [here](/docs/limits). • 8192 context
Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.
Designed for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.
See the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms). • 8192 context
Command is an instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models.
Use of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). • 4096 context
Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.
This is a normal offline LLM, but the [online version](/models/perplexity/llama-3-sonar-small-32k-online) of this model has Internet access. • 32768 context
Google's latest multimodal model, supporting image and video in text or chat prompts.
Optimized for language tasks including:
- Code generation
- Text generation
- Text editing
- Problem solving
- Recommendations
- Information extraction
- Data extraction or generation
- AI agents
Usage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).
#multimodal • 2800000 context
Stheno 8B 32K is a creative writing/roleplay model from [Sao10k](https://ko-fi.com/sao10k). It was trained at 8K context, then expanded to 32K context.
Compared to older Stheno version, this model is trained on:
- 2x the amount of creative writing samples
- Cleaned up roleplaying samples
- Fewer low quality samples • 32000 context
The NeverSleep team is back, with a Llama 3 8B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.
To enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.
Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).
Note: this is an extended-context version of [Llama 3 Lumimaid 8B](/models/neversleep/llama-3-lumimaid-8b). It may have higher prices and different outputs. • 24576 context
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](/models/meta-llama/llama-3-8b-instruct) and various fine-tuned models. It excels in general conversation, coding assistance, and mathematical reasoning.
- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/models/openchat/openchat-7b).
- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/models/openchat/openchat-8b).
#open-source • 8192 context
This model is currently powered by Mixtral-8X7B-v0.1, a sparse mixture of experts model with 12B active parameters. It has better reasoning, exhibits more capabilities, can produce and reason about code, and is multiligual, supporting English, French, German, Italian, and Spanish.
#moe • 32000 context
Dolphin 2.9 is designed for instruction following, conversational, and coding. This model is a finetune of [Mixtral 8x22B Instruct](/models/mistralai/mixtral-8x22b-instruct). It features a 64k context length and was fine-tuned with a 16k sequence length using ChatML templates.
This model is a successor to [Dolphin Mixtral 8x7B](/models/cognitivecomputations/dolphin-mixtral-8x7b).
The model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).
#moe #uncensored • 65536 context
Gemma by Google is an advanced, open-source language model family, leveraging the latest in decoder-only, text-to-text technology. It offers English language capabilities across text generation tasks like question answering, summarization, and reasoning. The Gemma 7B variant is comparable in performance to leading open source models.
Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms). • 8192 context
Gemma by Google is an advanced, open-source language model family, leveraging the latest in decoder-only, text-to-text technology. It offers English language capabilities across text generation tasks like question answering, summarization, and reasoning. The Gemma 7B variant is comparable in performance to leading open source models.
Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
Note: this is a higher-throughput version of [Gemma 7B](/models/google/gemma-7b-it). It may have higher prices and slightly different outputs. • 8192 context
Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.
It has demonstrated strong performance compared to leading closed-source models in human evaluations.
To read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).
Note: this is an extended-context version of [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct). It may have higher prices and different outputs. • 16384 context
A blazing fast vision-language model, FireLLaVA quickly understands both text and images. It achieves impressive chat skills in tests, and was designed to mimic multimodal GPT-4.
The first commercially permissive open source LLaVA model, trained entirely on open source LLM generated instruction following data. • 4096 context
The Yi series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). This is the base 34B parameter model. • 4096 context
LLaVA Yi 34B is an open-source model trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](/models/nousresearch/nous-hermes-yi-34b)
It was trained in December 2023. • 4096 context
Xwin-LM aims to develop and open-source alignment tech for LLMs. Our first release, built-upon on the [Llama2](/models/${Model.Llama_2_13B_Chat}) base models, ranked TOP-1 on AlpacaEval. Notably, it's the first to surpass [GPT-4](/models/${Model.GPT_4}) on this benchmark. The project will be continuously updated. • 8192 context
Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.
This is the online version of the [offline chat model](/models/perplexity/llama-3-sonar-large-32k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online • 28000 context