328 models available
TIIFalcon 7B InstructFalcon-7B-Instruct is a 7B parameters causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.
GoogleGemma 3 4BGemma 3 4B is Google's efficient Gemma 3 model supporting vision and text. Ideal for on-device applications requiring multimodal understanding with fast inference speeds.
MicrosoftPhi 3.5 Mini 4BPhi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
MicrosoftPhi 3 Mini 3.8BThe Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.
AlibabaQwen 2.5 Coder 1.5BQwen 2.5 Coder 1.5B is Alibaba's compact code-specific language model from the Qwen2.5 Coder series. Trained on 5.5T tokens including source code, text-code grounding, and synthetic data. Features improvements in code generation, reasoning, and fixing while maintaining general and math capabilities.
NVIDIANemotron Mini 4BNemotron-Mini-4B-Instruct is a model for generating responses for roleplaying, retrieval augmented generation, and function calling. It is a small language model (SLM) optimized through distillation, pruning and quantization for speed and on-device deployment. It is a fine-tuned version of nvidia/Minitron-4B-Base, which was pruned and distilled from Nemotron-4 15B using our LLM compression technique. This instruct model is optimized for roleplay, RAG QA, and function calling in English. It supports a context length of 4,096 tokens. This model is ready for commercial use.
MetaLlama 3.2 3BLlama 3.2 3B is Meta's compact multilingual text model optimized for edge and mobile deployment. Supports summarization, instruction following, and text generation with strong performance for its size class.
TinyLlamaTinyLlama 1.1BThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
GoogleGemma 2 2BGemma 2 2B is Google's lightweight model designed for on-device and edge deployment. Delivers strong text generation and reasoning performance at minimal resource cost.
MetaLlama 3.2 1BLlama 3.2 1B is Meta's smallest text model designed for on-device inference. Optimized for multilingual text generation, summarization, and instruction following on resource-constrained hardware.
AlibabaQwen 3 1.7BQwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
GoogleGemma 3 1BGemma 3 1B is Google's ultra-compact model from the Gemma 3 family. Optimized for mobile and edge inference with surprisingly capable text generation for its parameter count.
AlibabaQwen 3 0.6BQwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
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