はてなブックマーク

Parakeet TDT 0.6B V2 (En)","children":[{"id":"description","label":"Description:","children":[],"isValid":true,"title":"Description:"},{"id":"licenseterms-of-use","label":"License/Terms of Use:","children":[{"id":"deployment-geography","label":"Deployment Geography:","children":[],"isValid":true,"title":"Deployment Geography:"},{"id":"use-case","label":"Use Case:","children":[],"isValid":true,"tit

Summary This dataset presents a comprehensive taxonomy of 3307 values expressed by Claude (an AI assistant) across hundreds of thousands of real-world conversations. Using a novel privacy-preserving methodology, these values were extracted and classified without human reviewers accessing any conversation content. The dataset reveals patterns in how AI systems express values "in the wild" when inte

system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor

","cls_token":"","eos_token":"","mask_token":"","pad_token":"","sep_token":"","unk_token":"","use_default_system_prompt":false}},"createdAt":"2025-04-09T04:11:45.000Z","discussionsDisabled":false,"downloads":1255,"downloadsAllTime":1255,"id":"cl-nagoya/ruri-v3-310m","isLikedByUser":false,"availableInferenceProviders":[],"inference":"","lastModified":"2025-04-17T09:38:05.000Z","likes":18,"pipeline_

Generate any application with DeepSeek

Features DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects. Reasoning Capabilities Significant improvements in benchmark performance: MMLU-Pro: 75.9 → 81.2 (+5.3) GPQA: 59.1 → 68.4 (+9.3) AIME: 39.6 → 59.4 (+19.8) LiveCodeBench: 39.2 → 49.2 (+10.0) Front-End Web Development Improved the executability of the code More aesthetically pleasing

Translate Natural Language Prompts into Danbooru Tags

Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers. Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spann

Welcome to the 🤗 AI Agents Course The background of the image was generated using Scenario.com Welcome to the most exciting topic in AI today: Agents! This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents. This first unit will help you onboard: Discover the course’s syllabus. Choose the path you’re going to take (either self-audit or

Our Transformers Code Agent beats the GAIA benchmark! By July 1, 2024 • 77 DeepSeek's reasoning skills are probably particularly useful for something like this. But in my mind, particularly for academic research type tasks, the propaganda baked into the model is a non-starter. I tested out the new DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Unbiased model yesterday. It was a very crude test, but I

You can find the instructions to run these evaluations in the open-r1 repository. One observation we have made is the enormous size of the generations from the DeepSeek models, which makes even evaluating the model challenging. Here we show DeepSeek-R1 response lengths in the OpenThoughts dataset: Distribution of R1’s responses shows that they are on average very long with the average response bei

' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜to

","chat_template":"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in

Vision-language model series based on Qwen2.5

Controlling Language Model Generation with NVIDIA's LogitsProcessorZoo By December 23, 2024 • 38 Where is the evaluation numbers? without it you can’t call it reproduction. \n","updatedAt":"2025-01-28T07:55:21.483Z","author":{"_id":"64beee328496ee0fb62b7d3b","avatarUrl":"/avatars/c1cbffbd380310f7b51f3b452ecc4fe8.svg","fullname":"Factiverse AS","name":"facti","type":"user","isPro":false,"isHf":fals

' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜to

DeepSeek-R1-Distill-Qwen-32B-Japanese Model Description This is a Japanese finetuned model based on deepseek-ai/DeepSeek-R1-Distill-Qwen-32B. Usage from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese", device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained

' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and 'tool_calls' in message %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message['content'] is none %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['a

","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"unk_token":null,"chat_template":"{% if not add_generatio

Train 400x faster Static Embedding Models with Sentence Transformers TL;DR This blog post introduces a method to train static embedding models that run 100x to 400x faster on CPU than state-of-the-art embedding models, while retaining most of the quality. This unlocks a lot of exciting use cases, including on-device and in-browser execution, edge computing, low power and embedded applications. We

","chat_template":"{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %

You must be familiar with this message 🤬: RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 7.93 GiB total capacity; 6.00 GiB already allocated; 14.88 MiB free; 6.00 GiB reserved in total by PyTorch) While it's easy to see that GPU memory is full, understanding why and how to fix it can be more challenging. In this tutorial, we'll go step by step on how to visualize and unders

Discover amazing ML apps made by the community

0 \\)。\n\n目標は \\( y \\) の値を求める。\n\nまず、平面内の2点間の距離公式を思い出す：\n\\[\n\\text{距離} = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\n\\]\n\nこの公式を点 \\( A \\) と \\( B \\) に適用する：\n\\[\n13 = \\sqrt{(10 - 2)^2 + (y - 5)^2}\n\\]\n\n平方根の中を簡略化する：\n\\[\n13 = \\sqrt{(8)^2 + (y - 5)^2}\n\\]\n\\[\n13 = \\sqrt{64 + (y - 5)^2}\n\\]\n\n平方根を両辺から消すために、両辺を平方する：\n\\[\n13^2 = 64 + (y - 5)^2\n\\]\n\\[\n169 = 64 + (y - 5)^2\n\\]\n\n両辺から

","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"unk_token":null,"chat_template":"{% if not add_generatio

TL;DR This blog post introduces ModernBERT, a family of state-of-the-art encoder-only models representing improvements over older generation encoders across the board, with a 8192 sequence length, better downstream performance and much faster processing. ModernBERT is available as a slot-in replacement for any BERT-like models, with both a base (149M params) and large (395M params) model size. Cli