# Models that fit on one accelerator | SAVRN Model Hub Source: https://savrn.com/collections/models-that-run-on-one-accelerator Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Entries in this collection Model · Text generation ### [Llama-3.1-8B-Instruct](https://savrn.com/models/llama-3-1-8b-instruct) Published by [Meta Llama](https://savrn.com/model-publishers/meta-llama) The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.… Access requested at publisher llama3.1 8B parameters [View model](https://savrn.com/models/llama-3-1-8b-instruct) Model · Text generation ### [Mistral-7B-Instruct-v0.2](https://savrn.com/models/mistral-7b-instruct-v0-2) Published by [Mistral AI_](https://savrn.com/model-publishers/mistralai) The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2. Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1 - 32k context window (vs 8k context in v0.1) - Rope-theta = 1e6 For full details of this model please read our paper and release blog post. In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. This format is available as a chat template via the applychattemplate() method: - If you… Open weights apache-2.0 7.2B parameters [View model](https://savrn.com/models/mistral-7b-instruct-v0-2) Model · Text generation ### [Qwen2.5-7B-Instruct](https://savrn.com/models/qwen2-5-7b-instruct) Published by [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and… Open weights apache-2.0 7.6B parameters [View model](https://savrn.com/models/qwen2-5-7b-instruct) Model · Text generation ### [gemma-2-9b-it](https://savrn.com/models/gemma-2-9b-it) Published by [Google](https://savrn.com/model-publishers/google) [Responsible Generative AI Toolkit][rai-toolkit] [Gemma on Kaggle][kaggle-gemma] [Gemma on Vertex Model Garden][vertex-mg-gemma] Summary description and brief definition of inputs and outputs. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them… Access requested at publisher gemma 9.2B parameters [View model](https://savrn.com/models/gemma-2-9b-it) [Compare the first 4 models in this collection](https://savrn.com/models/compare?ids=llama-3-1-8b-instruct,mistral-7b-instruct-v0-2,qwen2-5-7b-instruct,gemma-2-9b-it) ## Explore More [Every collection](https://savrn.com/collections) · [The model directory](https://savrn.com/models) · [How a collection is decided](https://savrn.com/model-hub/methodology)