Displaying 1 to 30 of 65 repositories
Gemma 4: multimodal open AI models by Google, optimized for reasoning, coding, and long context.
3d
6.5K
397B MoE model with 17B activation for reasoning, coding, agents, and multimodal understanding
6d
50K+
5
397B-parameter MoE multimodal LLM with 17B active params, 262K context, 201 languages
6d
10K+
1
744B MoE language model with 40B active params for reasoning, coding, and agentic tasks (FP8)
2m
9.6K
3
Advanced coding agent model with 80B params (3B active MoE) for code generation and debugging
2m
10K+
1
Efficient 80B MoE coding model with 3B activated params, 256K context, and agentic capabilities
2m
10K+
1
Image generation model, uses a base latent diffusion model plus a refiner.
2m
10K+
5
GLM-4.7-Flash is a top 30B-A3B MoE, balancing strong performance with efficient deployment.
2m
10K+
4
GLM-4.7-Flash is a top 30B-A3B MoE, balancing strong performance with efficient deployment.
3m
10K+
1
Devstral Small 2 is an FP8 instruct LLM for agentic SWE tasks, codebase tooling, and SWE-bench.
3m
10K+
4
FunctionGemma is a 270M open model for fine-tuned, offline function-calling agents on small devices.
3m
5.5K
1
FunctionGemma is a 270M open model for fine-tuned, offline function-calling agents on small devices.
3m
8.4K
2
Kimi K2 Thinking: open-source agent with deep reasoning, stable tool use, fast INT4, 256k context.
4m
50K+
1
Kimi K2 Thinking: open-source agent with deep reasoning, stable tool use, fast INT4, 256k context.
4m
10K+
1
DeepSeek-V3.2 boosts efficiency and reasoning with DSA, scalable RL, agentic data—IMO/IOI wins.
4m
10K+
10
Ministral 3: compact vision-enabled model with near-24B performance, optimized for local edge use
4m
10K+
4
Ministral 3: compact vision-enabled model with near-24B performance, optimized for local edge use
4m
50K+
2
Multilingual reranking model for text retrieval, scoring document relevance across 119 languages.
4m
10K+
3
Multilingual reranking model for text retrieval, scoring document relevance across 119 languages.
4m
10K+
Snowflake’s Arctic-Embed v2.0 boosts multilingual retrieval and efficiency
5m
4.8K
Qwen3 Embedding: multilingual models for advanced text/ranking tasks like retrieval & clustering.
5m
10K+
1
Qwen3 Embedding: multilingual models for advanced text/ranking tasks like retrieval & clustering.
5m
10K+
OpenAI’s open-weight models designed for powerful reasoning, agentic tasks
5m
100K+
43
The most advanced Qwen model yet, with major gains in text, vision, video, and reasoning.
5m
100K+
9
Safety reasoning models for policy-based text classification and foundational safety tasks.
5m
10K+
2
Granite-4.0-nano: lightweight instruct model trained via SFT, RL, and merging on diverse data.
5m
9.5K
Granite-4.0-h-nano: lightweight instruct model trained via SFT, RL, and merging on diverse data.
5m
4.5K
1