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Qwen3 Highlights Qwen3 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: # Qwen3-235B-A22B ## Qw
# Qwen3-Coder-480B-A35B-Instruct ## Highlights Today, we're announcing **Qwen3-Coder**, our most agentic code model to date. **Qwen3-Coder** is available in multiple sizes, but we're excited to introduce its most powerful variant first: **Qwen3-Coder-480B-A35B-Instruct**. featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Cod
Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-0.6B ## Qwen3 Highlights Qwen3 is the latest generation of large language models
Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of Layers: 64 Number of Attention Heads (GQA): 64 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-32B ## Qwen3 Highlights Qwen3 is the late
Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Number of Layers: 40 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: 32,768 natively and . # Qwen3-14B ## Qwen3 Highlights Qwen3 is the latest generati
Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-8B ## Qwen3 Highlights Qwen3 is the latest
Qwen3 Highlights Qwen3 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: --- library_name: transformers licen
Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-4B ## Qwen3 Highlights Qwen3 is the latest gen
Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-1.7B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in
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Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-0.6B ## Qwen3 Highlights Qwen3 is the latest generation of large language models
Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of Layers: 64 Number of Attention Heads (GQA): 64 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-32B ## Qwen3 Highlights Qwen3 is the late
# Qwen3-Coder-480B-A35B-Instruct ## Highlights Today, we're announcing **Qwen3-Coder**, our most agentic code model to date. **Qwen3-Coder** is available in multiple sizes, but we're excited to introduce its most powerful variant first: **Qwen3-Coder-480B-A35B-Instruct**. featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Cod
Qwen3 Highlights Qwen3 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: # Qwen3-235B-A22B ## Qw
Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Number of Layers: 40 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: 32,768 natively and . # Qwen3-14B ## Qwen3 Highlights Qwen3 is the latest generati
Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-8B ## Qwen3 Highlights Qwen3 is the latest
Qwen3 Highlights Qwen3 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: --- library_name: transformers licen
Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-4B ## Qwen3 Highlights Qwen3 is the latest gen
Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-1.7B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in
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Qwen3 Highlights Qwen3 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: # Qwen3-235B-A22B ## Qw
# Qwen3-Coder-480B-A35B-Instruct ## Highlights Today, we're announcing **Qwen3-Coder**, our most agentic code model to date. **Qwen3-Coder** is available in multiple sizes, but we're excited to introduce its most powerful variant first: **Qwen3-Coder-480B-A35B-Instruct**. featuring the following key enhancements: - **Significant Performance** among open models on **Agentic Cod
Tech Blog | Paper Link (coming soon) ## 1. Model Introduction Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding t
Qwen3 Highlights Qwen3 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: --- library_name: transformers licen
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Reviews
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Qwen3-Coder is a 480B parameters non-thinking mode tuning model. Are there any comparison between Qwen3 vs Kimi2-Instruct on coding abilities, such as SWE-Bench etc?
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Strong performance with Qwen code command line support. The only drawback using the Qwen3 coder model is the high token consumption rate. Hopefully the price will drop to a reasonable level soon.
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Qwen 0.6B small size LLM is extremely powerful in realworld applications such as search and recommendation, query intent recognition, etc. And Qwen3 0.6B model is the SOTA one compared to previous counterparts such as Gemini and Llama small size LLM.
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Qwen3 32B model series are the most widely adopted and deployed model in industrial applications, which compromise of inference speed and performance. This updated version of Qwen3 32B model have the thinking mode and non-thinking mode, which supports both the common task of chat/text generation and more complex task of math, code generation, etc. On the AIME and many other math benchmarks, Qwen3 surpass many of the opensource counterpart.
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Qwen3 235B A22B model is more like an upgraded version of DeepSeek-R1. And it is also compared with Deepseek R1 model on multiple benchmarks of code and math. Personally, I don't Qwen3 is a huge upgrade compared to Gemini/OpenAI and Deepseek model, but more like a compromised version of complex thinking and realistic usage.
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Please leave your thoughts on the best and coolest AI Generated Images.
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Please leave your thoughts on free alternatives to Midjourney Stable Diffusion and other AI Image Generators.
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