diff --git a/assets/amazon.yaml b/assets/amazon.yaml
index d539cd24..70aad8fb 100644
--- a/assets/amazon.yaml
+++ b/assets/amazon.yaml
@@ -86,3 +86,76 @@
   prohibited_uses: ''
   monitoring: ''
   feedback: https://github.com/amazon-science/chronos-forecasting/discussions
+- type: model
+  name: Amazon Nova (Understanding)
+  organization: Amazon Web Services (AWS)
+  description: A new generation of state-of-the-art foundation models (FMs) that
+    deliver frontier intelligence and industry leading price performance, available
+    exclusively in Amazon Bedrock. Amazon Nova understanding models excel in Retrieval-Augmented
+    Generation (RAG), function calling, and agentic applications.
+  created_date: 2024-12-03
+  url: https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/
+  model_card: unknown
+  modality:
+    explanation: Amazon Nova understanding models accept text, image, or video inputs
+      to generate text output.
+    value: text, image, video; text
+  analysis: Amazon Nova Pro is capable of processing up to 300K input tokens and
+    sets new standards in multimodal intelligence and agentic workflows that require
+    calling APIs and tools to complete complex workflows. It achieves state-of-the-art
+    performance on key benchmarks including visual question answering ( TextVQA
+    ) and video understanding ( VATEX ).
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: All Amazon Nova models include built-in safety controls and creative
+    content generation models include watermarking capabilities to promote responsible
+    AI use.
+  access:
+    explanation: available exclusively in Amazon Bedrock
+    value: limited
+  license: unknown
+  intended_uses: You can build on Amazon Nova to analyze complex documents and videos,
+    understand charts and diagrams, generate engaging video content, and build sophisticated
+    AI agents, from across a range of intelligence classes optimized for enterprise
+    workloads.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
+- type: model
+  name: Amazon Nova (Creative Content Generation)
+  organization: Amazon Web Services (AWS)
+  description: A new generation of state-of-the-art foundation models (FMs) that
+    deliver frontier intelligence and industry leading price performance, available
+    exclusively in Amazon Bedrock.
+  created_date: 2024-12-03
+  url: https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/
+  model_card: unknown
+  modality:
+    explanation: Amazon creative content generation models accept text and image
+      inputs to generate image or video output.
+    value: text, image;image, video
+  analysis: Amazon Nova Canvas excels on human evaluations and key benchmarks such
+    as text-to-image faithfulness evaluation with question answering (TIFA) and
+    ImageReward.
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: All Amazon Nova models include built-in safety controls and creative
+    content generation models include watermarking capabilities to promote responsible
+    AI use.
+  access:
+    explanation: available exclusively in Amazon Bedrock
+    value: limited
+  license: unknown
+  intended_uses: You can build on Amazon Nova to analyze complex documents and videos,
+    understand charts and diagrams, generate engaging video content, and build sophisticated
+    AI agents, from across a range of intelligence classes optimized for enterprise
+    workloads.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/assets/anthropic.yaml b/assets/anthropic.yaml
index 1fe22119..758ed030 100644
--- a/assets/anthropic.yaml
+++ b/assets/anthropic.yaml
@@ -608,15 +608,17 @@
     speed of its predecessor, Claude 3 Opus, and is designed to tackle tasks like
     context-sensitive customer support, orchestrating multi-step workflows, interpreting
     charts and graphs, and transcribing text from images.
-  created_date: 2024-06-21
-  url: https://www.anthropic.com/news/claude-3-5-sonnet
+  created_date:
+    explanation: Claude 3.5 Sonnet updated on Oct. 22, initially released on June
+      20 of the same year.
+  value: 2024-10-22
+  url: https://www.anthropic.com/news/3-5-models-and-computer-use
   model_card: unknown
   modality: text; image, text
   analysis: The model has been evaluated on a range of tests including graduate-level
     reasoning (GPQA), undergraduate-level knowledge (MMLU), coding proficiency (HumanEval),
-    and standard vision benchmarks. In an internal agentic coding evaluation, Claude
-    3.5 Sonnet solved 64% of problems, outperforming the previous version, Claude
-    3 Opus, which solved 38%.
+    and standard vision benchmarks. Claude 3.5 Sonnet demonstrates state-of-the-art
+    performance on most benchmarks.
   size: Unknown
   dependencies: []
   training_emissions: Unknown
@@ -637,3 +639,38 @@
     integrated to ensure robustness of evaluations.
   feedback: Feedback on Claude 3.5 Sonnet can be submitted directly in-product to
     inform the development roadmap and improve user experience.
+- type: model
+  name: Claude 3.5 Haiku
+  organization: Anthropic
+  description: Claude 3.5 Haiku is Anthropic's fastest model, delivering advanced
+    coding, tool use, and reasoning capability, surpassing the previous Claude 3
+    Opus in intelligence benchmarks. It is designed for critical use cases where
+    low latency is essential, such as user-facing chatbots and code completions.
+  created_date: 2024-10-22
+  url: https://www.anthropic.com/claude/haiku
+  model_card: unknown
+  modality:
+    explanation: Claude 3.5 Haiku is available...initially as a text-only model
+      and with image input to follow.
+    value: text; unknown
+  analysis: Claude 3.5 Haiku offers strong performance and speed across a variety
+    of coding, tool use, and reasoning tasks. Also, it has been tested in extensive
+    safety evaluations and exceeded expectations in reasoning and code generation
+    tasks.
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: During Claude 3.5 Haiku’s development, we conducted extensive
+    safety evaluations spanning multiple languages and policy domains.
+  access:
+    explanation: Claude 3.5 Haiku is available across Claude.ai, our first-party
+      API, Amazon Bedrock, and Google Cloud’s Vertex AI.
+    value: open
+  license: unknown
+  intended_uses: Critical use cases where low latency matters, like user-facing
+    chatbots and code completions.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/assets/cohere.yaml b/assets/cohere.yaml
index bc7e0a2f..c5f86d73 100644
--- a/assets/cohere.yaml
+++ b/assets/cohere.yaml
@@ -592,3 +592,25 @@
   prohibited_uses: unknown
   monitoring: unknown
   feedback: https://huggingface.co/CohereForAI/aya-23-35B/discussions
+- type: model
+  name: Command R+
+  organization: Cohere
+  description: Command R+ is a state-of-the-art RAG-optimized model designed to
+    tackle enterprise-grade workloads, and is available first on Microsoft Azure.
+  created_date: 2024-04-04
+  url: https://cohere.com/blog/command-r-plus-microsoft-azure
+  model_card: unknown
+  modality: unknown
+  analysis: unknown
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: unknown
+  access: ''
+  license: unknown
+  intended_uses: unknown
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/assets/genmo.yaml b/assets/genmo.yaml
new file mode 100644
index 00000000..7425032a
--- /dev/null
+++ b/assets/genmo.yaml
@@ -0,0 +1,43 @@
+---
+- type: model
+  name: Mochi 1
+  organization: Genmo
+  description: Mochi 1 is an open-source video generation model designed to produce
+    high-fidelity motion and strong prompt adherence in generated videos, setting
+    a new standard for open video generation systems.
+  created_date: 2025-01-14
+  url: https://www.genmo.ai/blog
+  model_card: unknown
+  modality:
+    explanation: Mochi 1 generates smooth videos... Measures how accurately generated
+      videos follow the provided textual instructions
+    value: text; video
+  analysis: Mochi 1 sets a new best-in-class standard for open-source video generation.
+    It also performs very competitively with the leading closed models... We benchmark
+    prompt adherence with an automated metric using a vision language model as a
+    judge following the protocol in OpenAI DALL-E 3. We evaluate generated videos
+    using Gemini-1.5-Pro-002.
+  size:
+    explanation: featuring a 10 billion parameter diffusion model
+    value: 10B parameters
+  dependencies: [DDPM, DreamFusion, Emu Video, T5-XXL]
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: robust safety moderation protocols in the playground to ensure
+    that all video generations remain safe and aligned with ethical guidelines.
+  access:
+    explanation: open state-of-the-art video generation model... The weights and
+      architecture for Mochi 1 are open
+    value: open
+  license:
+    explanation: We're releasing the model under a permissive Apache 2.0 license.
+    value: Apache 2.0
+  intended_uses: Advance the field of video generation and explore new methodologies.
+    Build innovative applications in entertainment, advertising, education, and
+    more. Empower artists and creators to bring their visions to life with AI-generated
+    videos. Generate synthetic data for training AI models in robotics, autonomous
+    vehicles and virtual environments.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/assets/google.yaml b/assets/google.yaml
index 8bc851ae..94d73f70 100644
--- a/assets/google.yaml
+++ b/assets/google.yaml
@@ -1908,3 +1908,69 @@
   monitoring: unknown
   feedback: Encourages developer feedback to inform model improvements and future
     updates.
+- type: model
+  name: Veo 2
+  organization: Google DeepMind
+  description: Veo 2 is a state-of-the-art video generation model that creates videos
+    with realistic motion and high-quality output, up to 4K, with extensive camera
+    controls. It simulates real-world physics and offers advanced motion capabilities
+    with enhanced realism and fidelity.
+  created_date: 2024-12-16
+  url: https://deepmind.google/technologies/veo/veo-2/
+  model_card: unknown
+  modality:
+    explanation: Our state-of-the-art video generation model ... text-to-image model
+      Veo 2
+    value: text; video
+  analysis: Veo 2 outperforms other leading video generation models, based on human
+    evaluations of its performance.
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: Veo 2 includes features that enhance realism, fidelity, detail,
+    and artifact reduction to ensure high-quality output.
+  access: limited
+  license: unknown
+  intended_uses: Creating high-quality videos with realistic motion, different styles,
+    camera controls, shot styles, angles, and movements.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
+
+- type: model
+  name: Gemini 2.0
+  organization: Google DeepMind
+  description: Google DeepMind introduces Gemini 2.0, a new AI model designed for
+    the 'agentic era.'
+  created_date: 2024-12-11
+  url: https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/#ceo-message
+  model_card: unknown
+  modality:
+    explanation: The first model built to be natively multimodal, Gemini 1.0 and
+      1.5 drove big advances with multimodality and long context to understand information
+      across text, video, images, audio and code...
+    value: text, video, image, audio; image, text
+  analysis: unknown
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware:
+    explanation: It’s built on custom hardware like Trillium, our sixth-generation
+      TPUs.
+    value: custom hardware like Trillium, our sixth-generation TPUs
+  quality_control: Google is committed to building AI responsibly, with safety and
+    security as key priorities.
+  access:
+    explanation: Gemini 2.0 Flash is available to developers and trusted testers,
+      with wider availability planned for early next year.
+    value: limited
+  license: unknown
+  intended_uses: Develop more agentic models, meaning they can understand more about
+    the world around you, think multiple steps ahead, and take action on your behalf,
+    with your supervision.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/assets/ibm.yaml b/assets/ibm.yaml
index 260b3a9f..368680ae 100644
--- a/assets/ibm.yaml
+++ b/assets/ibm.yaml
@@ -75,3 +75,45 @@
   prohibited_uses: ''
   monitoring: ''
   feedback: ''
+- type: model
+  name: IBM Granite 3.0
+  organization: IBM
+  description: IBM Granite 3.0 models deliver state-of-the-art performance relative
+    to model size while maximizing safety, speed and cost-efficiency for enterprise
+    use cases.
+  created_date: 2024-10-21
+  url: https://www.ibm.com/new/ibm-granite-3-0-open-state-of-the-art-enterprise-models
+  model_card: unknown
+  modality:
+    explanation: IBM Granite 3.0 8B Instruct model for classic natural language
+      use cases including text generation, classification, summarization, entity
+      extraction and customer service chatbots
+    value: text; text
+  analysis: Granite 3.0 8B Instruct matches leading similarly-sized open models
+    on academic benchmarks while outperforming those peers on benchmarks for enterprise
+    tasks and safety.
+  size:
+    explanation: 'Dense, general purpose LLMs: Granite-3.0-8B-Instruct'
+    value: 8B parameters
+  dependencies: [Hugging Face’s OpenLLM Leaderboard v2]
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: The entire Granite family of models are trained on carefully
+    curated enterprise datasets, filtered for objectionable content with critical
+    concerns like governance, risk, privacy and bias mitigation in mind
+  access:
+    explanation: In keeping with IBM’s strong historical commitment to open source
+      , all Granite models are released under the permissive Apache 2.0 license
+    value: open
+  license:
+    explanation: In keeping with IBM’s strong historical commitment to open source
+      , all Granite models are released under the permissive Apache 2.0 license
+    value: Apache 2.0
+  intended_uses: classic natural language use cases including text generation, classification,
+    summarization, entity extraction and customer service chatbots, programming
+    language use cases such as code generation, code explanation and code editing,
+    and for agentic use cases requiring tool calling
+  prohibited_uses: unknown
+  monitoring: ''
+  feedback: unknown
diff --git a/assets/inflection.yaml b/assets/inflection.yaml
index 4f82b1f9..d18db5ce 100644
--- a/assets/inflection.yaml
+++ b/assets/inflection.yaml
@@ -93,3 +93,29 @@
   prohibited_uses: ''
   monitoring: ''
   feedback: none
+- type: model
+  name: Inflection 3.0
+  organization: Inflection AI
+  description: Inflection for Enterprise, powered by our industry-first, enterprise-grade
+    AI system, Inflection 3.0.
+  created_date: 2024-10-07
+  url: https://inflection.ai/blog/enterprise
+  model_card: unknown
+  modality: unknown
+  analysis: unknown
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: unknown
+  access:
+    explanation: Developers can now access Inflection AI’s Large Language Model
+      through its new commercial API.
+    value: open
+  license: unknown
+  intended_uses: unknown
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: So please drop us a line. We want to keep hearing from enterprises about
+    how we can help solve their challenges and make AI a reality for their business.
diff --git a/assets/meta.yaml b/assets/meta.yaml
index 0b7653ec..8b337691 100644
--- a/assets/meta.yaml
+++ b/assets/meta.yaml
@@ -891,3 +891,51 @@
   prohibited_uses: Unknown
   monitoring: Unknown
   feedback: Unknown
+- type: model
+  name: Llama 3.3
+  organization: Meta
+  description: The Meta Llama 3.3 multilingual large language model (LLM) is an
+    instruction tuned generative model in 70B (text in/text out).
+  created_date: 2024-12-06
+  url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+  model_card: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+  modality:
+    explanation: The Llama 3.3 instruction tuned text only model is optimized for
+      multilingual dialogue use cases.
+    value: text; text
+  analysis: Unknown
+  size:
+    explanation: The Meta Llama 3.3 multilingual large language model (LLM) is an
+      instruction tuned generative model in 70B (text in/text out).
+    value: 70B parameters
+  dependencies: []
+  training_emissions:
+    explanation: Training Greenhouse Gas Emissions Estimated total location-based
+      greenhouse gas emissions were 11,390 tons CO2eq for training.
+    value: 11,390 tons CO2eq
+  training_time:
+    explanation: Training utilized a cumulative of 39.3M GPU hours of computation
+      on H100-80GB (TDP of 700W) type hardware.
+    value: 39.3M GPU hours
+  training_hardware:
+    explanation: Training utilized a cumulative of 39.3M GPU hours of computation
+      on H100-80GB (TDP of 700W) type hardware.
+    value: H100-80GB (TDP of 700W) type hardware
+  quality_control: Used "supervised fine-tuning (SFT) and reinforcement learning
+    with human feedback (RLHF) to align with human preferences for helpfulness and
+    safety."
+  access:
+    explanation: Future versions of the tuned models will be released as we improve
+      model safety with community feedback.
+    value: open
+  license:
+    explanation: A custom commercial license, the Llama 3.3 Community License Agreement
+    value: Llama 3.3 Community License Agreement
+  intended_uses: Intended for commercial and research use in multiple languages.
+    Instruction tuned text only models are intended for assistant-like chat.
+  prohibited_uses: Use in any manner that violates applicable laws or regulations
+    (including trade compliance laws). Use in any other way that is prohibited by
+    the Acceptable Use Policy and Llama 3.3 Community License.
+  monitoring: Unknown
+  feedback: Instructions on how to provide feedback or comments on the model can
+    be found in the model README.
diff --git a/assets/microsoft.yaml b/assets/microsoft.yaml
index 137996a4..1c7dc960 100644
--- a/assets/microsoft.yaml
+++ b/assets/microsoft.yaml
@@ -1032,3 +1032,38 @@
     use case, particularly for high risk scenarios.
   monitoring: Unknown
   feedback: Unknown
+- type: model
+  name: Phi-4
+  organization: Microsoft
+  description: the latest small language model in Phi family, that offers high quality
+    results at a small size (14B parameters).
+  created_date: 2024-12-13
+  url: https://techcommunity.microsoft.com/blog/aiplatformblog/introducing-phi-4-microsoft%E2%80%99s-newest-small-language-model-specializing-in-comple/4357090
+  model_card: unknown
+  modality:
+    explanation: Today we are introducing Phi-4 , our 14B parameter state-of-the-art
+      small language model (SLM) that excels at complex reasoning in areas such
+      as math, in addition to conventional language processing.
+    value: text; text
+  analysis: Phi-4 outperforms comparable and larger models on math related reasoning.
+  size:
+    explanation: a small size (14B parameters).
+    value: 14B parameters
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: Building AI solutions responsibly is at the core of AI development
+    at Microsoft. We have made our robust responsible AI capabilities available
+    to customers building with Phi models.
+  access:
+    explanation: Phi-4 is available on Azure AI Foundry and on Hugging Face.
+    value: open
+  license: unknown
+  intended_uses: Specialized in complex reasoning, particularly good at math problems
+    and high-quality language processing.
+  prohibited_uses: unknown
+  monitoring: Azure AI evaluations in AI Foundry enable developers to iteratively
+    assess the quality and safety of models and applications using built-in and
+    custom metrics to inform mitigations.
+  feedback: unknown
diff --git a/assets/mistral.yaml b/assets/mistral.yaml
index 3c78ac23..7fe7ce5a 100644
--- a/assets/mistral.yaml
+++ b/assets/mistral.yaml
@@ -192,3 +192,77 @@
   monitoring: Unknown
   feedback: Feedback is likely expected to be given through the HuggingFace platform
     where the model's weights are hosted or directly to the Mistral AI team.
+- type: model
+  name: Pixtral Large
+  organization: Mistral AI
+  description: Pixtral Large is the second model in our multimodal family and demonstrates
+    frontier-level image understanding. Particularly, the model is able to understand
+    documents, charts and natural images, while maintaining the leading text-only
+    understanding of Mistral Large 2.
+  created_date: 2024-11-18
+  url: https://mistral.ai/news/pixtral-large/
+  model_card: unknown
+  modality:
+    explanation: Pixtral Large is the second model in our multimodal family and
+      demonstrates frontier-level image understanding.
+    value: text, image; text
+  analysis: We evaluate Pixtral Large against frontier models on a set of standard
+    multimodal benchmarks, through a common testing harness.
+  size:
+    explanation: Today we announce Pixtral Large, a 124B open-weights multimodal
+      model.
+    value: 124B parameters
+  dependencies: [Mistral Large 2]
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: unknown
+  access:
+    explanation: The model is available under the Mistral Research License (MRL)
+      for research and educational use; and the Mistral Commercial License for experimentation,
+      testing, and production for commercial purposes.
+    value: open
+  license:
+    explanation: The model is available under the Mistral Research License (MRL)
+      for research and educational use; and the Mistral Commercial License for experimentation,
+      testing, and production for commercial purposes.
+    value: Mistral Research License (MRL), Mistral Commercial License
+  intended_uses: RAG and agentic workflows, making it a suitable choice for enterprise
+    use cases such as knowledge exploration and sharing, semantic understanding
+    of documents, task automation, and improved customer experiences.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
+
+- type: model
+  name: Codestral 25.01
+  organization: Mistral AI
+  description: Lightweight, fast, and proficient in over 80 programming languages,
+    Codestral is optimized for low-latency, high-frequency usecases and supports
+    tasks such as fill-in-the-middle (FIM), code correction and test generation.
+  created_date: 2025-01-13
+  url: https://mistral.ai/news/codestral-2501/
+  model_card: unknown
+  modality:
+    explanation: it for free in Continue for VS Code or JetBrains
+    value: text; text
+  analysis: Benchmarks We have benchmarked the new Codestral with the leading sub-100B
+    parameter coding models that are widely considered to be best-in-class for FIM
+    tasks.
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: unknown
+  access:
+    explanation: The API is also available on Google Cloud’s Vertex AI, in private
+      preview on Azure AI Foundry, and coming soon to Amazon Bedrock.
+    value: closed
+  license: unknown
+  intended_uses: Highly capable coding companion, regularly boosting productivity
+    several times over.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: We can’t wait to hear your experience! Try it now Try it on Continue.dev
+    with VsCode or JetBrains
diff --git a/assets/openx.yaml b/assets/openx.yaml
index 25d61646..afaf3ab6 100644
--- a/assets/openx.yaml
+++ b/assets/openx.yaml
@@ -77,3 +77,79 @@
   prohibited_uses: none
   monitoring: unknown
   feedback: none
+- type: model
+  name: GPT-4o
+  organization: OpenAI
+  description: GPT-4o is an autoregressive omni model that accepts a combination
+    of text, audio, image, and video as input and produces any combination of text,
+    audio, and image outputs. It is trained end-to-end across text, vision, and
+    audio, focusing on multimodal capabilities.
+  created_date: 2024-08-08
+  url: https://arxiv.org/pdf/2410.21276
+  model_card: unknown
+  modality:
+    explanation: '...accepts as input any combination of text, audio, image, and
+      video and generates any combination of text, audio, and image outputs.'
+    value: text, audio, image, video; text, audio, image
+  analysis: GPT-4o underwent evaluations that included the Preparedness Framework,
+    external red teaming, and third-party assessments to ensure safe and aligned
+    deployment. The evaluations focused on identifying and mitigating potential
+    risks across its capabilities, especially speech-to-speech functionality.
+  size: unknown
+  dependencies: [Shutterstock]
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: Quality and safety measures included prior risk assessments,
+    post-training mitigation, moderation tools, advanced data filtering, and external
+    red teaming efforts with experts to evaluate potential risks like bias, discrimination,
+    and information harms.
+  access:
+    explanation: we are sharing the GPT-4o System Card, which includes our Preparedness
+      Framework evaluations.
+    value: limited
+  license: unknown
+  intended_uses: Use in multimodal applications requiring understanding and generation
+    of combinations of text, audio, and image outputs, better performance on non-English
+    languages, and enhanced vision and audio understanding.
+  prohibited_uses: Uses that could involve bias, discrimination, harmful content,
+    or violation of usage policies.
+  monitoring: Continuous monitoring and enforcement, providing moderation tools
+    and transparency reports, and gathering feedback from users.
+  feedback: unknown
+
+- type: model
+  name: OpenAI o1
+  organization: OpenAI
+  description: OpenAI o1 is a new series of AI models designed to spend more time
+    thinking before they respond. They can reason through complex tasks and solve
+    harder problems than previous models in science, coding, and math.
+  created_date: 2024-09-12
+  url: https://openai.com/o1/
+  model_card: unknown
+  modality: text; text
+  analysis: Evaluated on challenging benchmark tasks in physics, chemistry, and
+    biology. In a qualifying exam for the International Mathematics Olympiad (IMO),
+    GPT-4o correctly solved only 13% of problems, while the reasoning model o1 scored
+    83%.
+  size: unknown
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: To match the new capabilities of these models, OpenAI has bolstered
+    safety work, internal governance, and federal government collaboration. This
+    includes rigorous testing and evaluations using their Preparedness Framework⁠(opens
+    in a new window), best-in-class red teaming, and board-level review processes,
+    including by OpenAI's Safety & Security Committee.
+  access: limited
+  license: unknown
+  intended_uses: These enhanced reasoning capabilities may be particularly useful
+    if you’re tackling complex problems in science, coding, math, and similar fields.
+    For example, o1 can be used by healthcare researchers to annotate cell sequencing
+    data, by physicists to generate complicated mathematical formulas needed for
+    quantum optics, and by developers in all fields to build and execute multi-step
+    workflows.
+  prohibited_uses: ''
+  monitoring: ''
+  feedback: unknown
diff --git a/assets/stability_ai.yaml b/assets/stability_ai.yaml
index 3ff8188c..0972b1bb 100644
--- a/assets/stability_ai.yaml
+++ b/assets/stability_ai.yaml
@@ -113,3 +113,43 @@
   monitoring: Unknown
   feedback: Information on any downstream issues with the model can be reported
     to Stability AI through their support request system.
+- type: model
+  name: Stable Diffusion 3.5
+  organization: Stability AI
+  description: Stable Diffusion 3.5 reflects our commitment to empower builders
+    and creators with tools that are widely accessible, cutting-edge, and free for
+    most use cases.
+  created_date: 2023-10-29
+  url: https://stability.ai/news/introducing-stable-diffusion-3-5
+  model_card: unknown
+  modality:
+    explanation: Capable of generating a wide range of styles and aesthetics like
+      3D, photography, painting, line art, and virtually any visual style imaginable.
+    value: text; image
+  analysis: Our analysis shows that Stable Diffusion 3.5 Large leads the market
+    in prompt adherence and rivals much larger models in image quality.
+  size:
+    explanation: At 8.1 billion parameters, with superior quality and prompt adherence,
+      this base model is the most powerful in the Stable Diffusion family.
+    value: 8.1B parameters
+  dependencies: []
+  training_emissions: unknown
+  training_time: unknown
+  training_hardware: unknown
+  quality_control: We believe in safe, responsible AI practices and take deliberate
+    measures to ensure Integrity starts at the early stages of development.
+  access:
+    explanation: This open release includes multiple model variants, including Stable
+      Diffusion 3.5 Large and Stable Diffusion 3.5 Large Turbo, and as of October
+      29th, Stable Diffusion 3.5 Medium.
+    value: open
+  license:
+    explanation: This open release includes multiple variants that are customizable,
+      run on consumer hardware, and are available for use under the permissive Stability
+      AI Community License.
+    value: Stability AI Community
+  intended_uses: This model is ideal for professional use cases at 1 megapixel resolution.
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: We look forward to hearing your feedback on Stable Diffusion 3.5 and
+    seeing what you create with the models.
diff --git a/assets/unknown.yaml b/assets/unknown.yaml
new file mode 100644
index 00000000..c2f323c3
--- /dev/null
+++ b/assets/unknown.yaml
@@ -0,0 +1,40 @@
+---
+- type: model
+  name: DeepSeek-V3
+  organization: DeepSeek
+  description: DeepSeek-V3 is a Mixture-of-Experts (MoE) language model with 671B
+    total parameters and 37B activated per token. It utilizes Multi-head Latent
+    Attention (MLA) and adopts innovative strategies for improved performance, such
+    as an auxiliary-loss-free load balancing and a multi-token prediction training
+    objective. Comprehensive evaluations show it achieves performance comparable
+    to leading closed-source models.
+  created_date: 2025-01-14
+  url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  model_card: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  modality: unknown
+  analysis: Comprehensive evaluations reveal that DeepSeek-V3 outperforms other
+    open-source models and achieves performance comparable to leading closed-source
+    models.
+  size:
+    explanation: a strong Mixture-of-Experts (MoE) language model with 671B total
+      parameters with 37B activated for each token.
+    value: 671B parameters (sparse)
+  dependencies: [DeepSeek-R1]
+  training_emissions: unknown
+  training_time:
+    explanation: DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
+    value: 2.788M GPU hours
+  training_hardware:
+    explanation: DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
+    value: H800 GPUs
+  quality_control: Post-training includes knowledge distillation from the DeepSeek-R1
+    model, incorporating verification and reflection patterns to enhance reasoning
+    performance.
+  access:
+    explanation: producing the currently strongest open-source base model.
+    value: open
+  license: MIT
+  intended_uses: unknown
+  prohibited_uses: unknown
+  monitoring: unknown
+  feedback: unknown
diff --git a/js/main.js b/js/main.js
index 6b7611e5..3809eae1 100644
--- a/js/main.js
+++ b/js/main.js
@@ -635,9 +635,11 @@ function loadAssetsAndRenderPageContent() {
 
   const paths = [
     'assets/adept.yaml',
+    'assets/genmo.yaml',
     'assets/mila.yaml',
     'assets/soochow.yaml',
     'assets/baichuan.yaml',
+    'assets/unknown.yaml',
     'assets/xwin.yaml',
     'assets/mistral.yaml',
     'assets/adobe.yaml',