Models.
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qwen3-tts
Alibaba Qwen
Qwen3-TTS: Qwen's open speech model. Preset voices, 10 languages, tone control from a text instruction, and a variant that clones a voice from 3 seconds of audio.
- size
- 1.7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#tts #speech #multilingual
olmo-3-7b-instruct
Ai2
Open language model from Ai2, pre-trained on Dolma 3 and post-trained on the Dolci datasets; all code and checkpoints are released.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct
qwen3-vl-8b-instruct
Qwen
Qwen3-VL 8B Instruct vision-language model with visual agent, spatial perception and OCR in 32 languages; native 256K context, expandable to 1M.
- size
- 8B
- vram
- ~7 GB
- license
- Apache-2.0
29% of a 24 GB card
#vision
lfm2-8b-a1b
Liquid AI
Liquid AI on-device hybrid MoE with 8.3B total and 1.5B active parameters and a 32,768-token context.
- size
- 8B-A1B
- vram
- ~6 GB
- license
- LFM Open License v1.0
25% of a 24 GB card
#chat #instruct #moe
granite-4-0-h-tiny
IBM
IBM 7B-parameter (1B active) hybrid long-context instruct model with improved instruction following and tool calling.
- size
- 7B-A1B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct #moe
granite-4.0-h-small
IBM
32B-parameter long-context instruct model with improved instruction following and tool calling, finetuned from Granite-4.0-H-Small-Base.
- size
- 32B
- vram
- ~21 GB
- license
- Apache-2.0
88% of a 24 GB card
#chat #instruct #large
seed-oss-36b-instruct
ByteDance Seed
ByteDance Seed open-source LLM for long-context, reasoning and agent tasks, with flexible thinking-budget control and native 512K context.
- size
- 36B
- vram
- ~23 GB
- license
- Apache-2.0
96% of a 24 GB card
#reasoning #large
qwen3-4b-instruct-2507
Alibaba Qwen
Updated Qwen3-4B non-thinking model with better instruction following, reasoning and coding; 256K native context, no think blocks.
- size
- 4B
- vram
- ~4 GB
- license
- Apache-2.0
17% of a 24 GB card
#chat #instruct #small
qwen3-30b-a3b-instruct-2507
Alibaba Qwen
Qwen3-30B-A3B non-thinking update: 30.5B-parameter MoE with 3.3B active, 128 experts and 256K native context.
- size
- 30B-A3B
- vram
- ~20 GB
- license
- Apache-2.0
83% of a 24 GB card
#chat #instruct #moe
ernie-4-5-21b-a3b
Baidu
Text MoE post-trained model with 21B total and 3B activated parameters per token and a 131072-token context length.
- size
- 21B-A3B
- vram
- ~15 GB
- license
- Apache-2.0
63% of a 24 GB card
#chat #instruct #moe
gemma3n-e4b
Google's Gemma 3n E4B instruction-tuned model for low-resource devices, effective 4B parameters, 32K context; this GGUF is text-only.
- size
- E4B
- vram
- ~6 GB
- license
- Gemma
25% of a 24 GB card
#chat #instruct #small
mistral-small-3.2-24b
Mistral AI
Mistral Small 3.1 update with better instruction following, fewer repetitive outputs and more robust function calling; text and image input.
- size
- 24B
- vram
- ~17 GB
- license
- Apache-2.0
71% of a 24 GB card
#chat #instruct #vision
qwen3-0-6b
Alibaba Qwen
Qwen3 0.6B causal language model with switchable thinking and non-thinking modes, 32K context and 100+ languages.
- size
- 0.6B
- vram
- ~2 GB
- license
- Apache-2.0
8% of a 24 GB card
#chat #instruct #small
qwen3-1-7b
Alibaba Qwen
Qwen3 1.7B causal language model with switchable thinking and non-thinking modes, 32K context and 100+ languages.
- size
- 1.7B
- vram
- ~3 GB
- license
- Apache-2.0
13% of a 24 GB card
#chat #instruct #small
qwen3-14b
Alibaba Qwen
Qwen3 14.8B dense model switching between thinking and non-thinking modes; 32K context, 131K with YaRN, 100+ languages.
- size
- 14B
- vram
- ~10 GB
- license
- Apache-2.0
42% of a 24 GB card
#chat #instruct
qwen3-32b
Alibaba Qwen
Qwen3 32.8B dense model switching between thinking and non-thinking modes; 32K context, 131K with YaRN, 100+ languages.
- size
- 32B
- vram
- ~21 GB
- license
- Apache-2.0
88% of a 24 GB card
#chat #instruct #large
mixtral
Mistral AI
Mixtral 8x7B — a sparse mixture-of-experts model offering strong quality at high inference efficiency.
- size
- 8x7B
- vram
- ~28 GB
- license
- Apache-2.0
1.2× a 24 GB card
#moe #reasoning #instruct
qwen2.5-vl-32b-instruct
Qwen
Instruction-tuned 32B Qwen2.5-VL vision-language model for visual understanding, agentic use, long-video comprehension and structured outputs.
- size
- 32B
- vram
- ~22 GB
- license
- Apache-2.0
92% of a 24 GB card
#vision #large
gemma3-4b
Google's multimodal Gemma 3 4B instruction-tuned model: text and image input, text output, 128K context, 140+ languages.
- size
- 4B
- vram
- ~5 GB
- license
- Gemma
21% of a 24 GB card
#chat #instruct #vision
gemma3-27b
Google's multimodal Gemma 3 27B instruction-tuned model: text and image input, text output, 128K context, 140+ languages.
- size
- 27B
- vram
- ~19 GB
- license
- Gemma
79% of a 24 GB card
#chat #instruct #vision
smolvlm2-2.2b-instruct
Hugging Face
Lightweight multimodal model that processes video, image and text inputs to generate text; about 5.2GB of GPU RAM for video inference.
- size
- 2.2B
- vram
- ~4 GB
- license
- Apache-2.0
17% of a 24 GB card
#vision #small
phi4
Microsoft
14B dense decoder-only Transformer trained on synthetic and filtered public data, built for high-quality reasoning in constrained environments.
- size
- 14B
- vram
- ~10 GB
- license
- MIT
42% of a 24 GB card
#chat #instruct
llama3.3-70b
Meta
Meta's 70B multilingual instruction-tuned text model with 128k context, optimized for multilingual dialogue use cases.
- size
- 70B
- vram
- ~44 GB
- license
- Llama 3.3 Community
1.8× a 24 GB card
#chat #instruct #large
qwen2.5-coder-32b
Alibaba Qwen
Code-specific instruction-tuned 32B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 32B
- vram
- ~21 GB
- license
- Apache-2.0
88% of a 24 GB card
#code #instruct #large
qwen2.5-coder-14b-instruct
Qwen
Instruction-tuned 14B code model with improved code generation, reasoning and fixing, and long-context support up to 128K tokens.
- size
- 14B
- vram
- ~10 GB
- license
- Apache-2.0
42% of a 24 GB card
#code #instruct
smollm2-1-7b-instruct
Hugging Face
1.7B compact instruct model trained on 11T tokens with SFT and DPO; supports rewriting, summarization and function calling.
- size
- 1.7B
- vram
- ~3 GB
- license
- Apache-2.0
13% of a 24 GB card
#chat #instruct #small
llama3.2
Meta
Meta's 3B multilingual instruction-tuned text model, optimized for dialogue, agentic retrieval and summarization tasks.
- size
- 3B
- vram
- ~3 GB
- license
- Llama 3.2 Community
13% of a 24 GB card
#chat #instruct #small
llama3.2-1b
Meta
Meta's 1B multilingual instruction-tuned text model, optimized for dialogue, agentic retrieval and summarization tasks.
- size
- 1B
- vram
- ~2 GB
- license
- Llama 3.2 Community
8% of a 24 GB card
#chat #instruct #small
qwen2.5-coder
Alibaba Qwen
Code-specific instruction-tuned 7B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#code #instruct
qwen2.5
Alibaba Qwen
Instruction-tuned 7B Qwen2.5 model with improved coding, maths, instruction following and JSON output; 29+ languages.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct
yi-coder-9b-chat
01.AI
Open-source 9B code chat model with a 128K maximum context length, supporting 52 major programming languages.
- size
- 9B
- vram
- ~7 GB
- license
- Apache-2.0
29% of a 24 GB card
#code #instruct
hermes-3-llama-3-1-8b
Nous Research
Generalist Llama-3.1 8B fine-tune with ChatML format, function calling, structured output, roleplay and reasoning improvements.
- size
- 8B
- vram
- ~6 GB
- license
- Llama 3
25% of a 24 GB card
#chat #instruct
llama3.1
Meta
Meta's 8B multilingual instruction-tuned text model with 128k context, optimized for assistant-like chat.
- size
- 8B
- vram
- ~6 GB
- license
- Llama 3.1 Community
25% of a 24 GB card
#chat #instruct
mistral-nemo-12b
Mistral AI / NVIDIA
12B instruct model trained jointly by Mistral AI and NVIDIA with a 128k context window on multilingual and code data.
- size
- 12B
- vram
- ~9 GB
- license
- Apache-2.0
38% of a 24 GB card
#chat #instruct
gemma2
Google's instruction-tuned Gemma 2 9B, trained on 8T tokens of web documents, code and mathematics; no system prompt support.
- size
- 9B
- vram
- ~7 GB
- license
- Gemma
29% of a 24 GB card
#chat #instruct
mistral
Mistral AI
Instruct fine-tuned Mistral-7B-v0.3 with extended 32768-token vocabulary, v3 tokenizer and function calling support.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct
starcoder2
BigCode
Code LLM self-aligned from StarCoder2-15B with a fully permissive, transparent pipeline and no human annotations or distilled data.
- size
- 15B
- vram
- ~11 GB
- license
- BigCode OpenRAIL-M
46% of a 24 GB card
#code #instruct
nomic-embed-text
Nomic AI
English text embedding model with Matryoshka representation learning for resizable embeddings; requires task instruction prefixes.
- size
- 137M
- vram
- ~2 GB
- license
- Apache-2.0
8% of a 24 GB card
#embedding #small