Splitting out the command r7b content.

fern/pages/models/the-command-family-of-models/command-r-plus.mdx

@@ -8,10 +8,9 @@ description: >-
 image: ../../../assets/images/edb3e49-cohere_meta_image.jpg
 keywords: 'generative AI, Cohere, large language models'
 createdAt: 'Thu Apr 04 2024 08:03:47 GMT+0000 (Coordinated Universal Time)'
-updatedAt: 'Thu Jun 06 2024 22:58:37 GMT+0000 (Coordinated Universal Time)'
+updatedAt: 'Wed Dec 18 2024 14:16:00 GMT+0000 (Coordinated Universal Time)'
 ---
 
-
 Command R+ is Cohere's newest large language model, optimized for conversational interaction and long-context tasks. It aims at being extremely performant, enabling companies to move beyond proof of concept and into production.
 
 We recommend using Command R+ for those workflows that lean on complex RAG functionality and [multi-step tool use (agents)](/docs/multi-hop-tool-use). Command R, on the other hand, is great for simpler [retrieval augmented generation](/docs/retrieval-augmented-generation-rag) (RAG) and [single-step tool use](/docs/tool-use) tasks, as well as applications where price is a major consideration.
@@ -21,20 +20,10 @@ For information on toxicity, safety, and using this model responsibly check out
 ### Model Details
 | Model Name               | Description                                                                                                                                                                                                                                         | Modality             | Context Length | Maximum Output Tokens | Endpoints              |
 |--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|----------------|-----------------------|------------------------|
-| `command-r7b-12-2024`    | `command-r7b-12-2024` is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps.                                            | Text                 | 128k           | 4k                    | [Chat](/reference/chat)|
 | `command-r-plus-08-2024` | `command-r-plus-08-2024` is an update of the Command R+ model, delivered in August 2024.                                                                                                                                                            | Text                 | 128k           | 4k                    | [Chat](/reference/chat)|
 | `command-r-plus-04-2024` | Command R+ is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It is best suited for complex RAG workflows and multi-step tool use.   | Text                 | 128k           | 4k                    | [Chat](/reference/chat)|
 | `command-r-plus`         | `command-r-plus` is an alias for `command-r-plus-04-2024`, so if you use `command-r-plus` in the API, that's the model you're pointing to.                                                                                                          | Text                 | 128k           | 4k                    | [Chat](/reference/chat)|
 
-## Command R7B December 2024 Release
-Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused [large language models](https://docs.cohere.com/docs/introduction-to-large-language-models) (LLMs). With a context window of 128K, Command R7B offers state-of-the-art performance across a variety of real-world tasks, and is designed for use cases in which speed, cost, and compute are important. Specifically, Command R7B is excellent for:
-
-- RAG - [Retrieval Augmented Generation](https://docs.cohere.com/docs/retrieval-augmented-generation-rag) (RAG) refers to the practice of ‘grounding’ model outputs in external data sources, which can increase accuracy. Command R7B is exceptionally good at generating responses in conversational tasks, attending over long inputs, and extracting and manipulating numerical information in financial settings.
-- Tool-use - With [tool use](https://docs.cohere.com/docs/tool-use), Command models can be given tools such as search engines, APIs, vector databases, etc., which can expand their baseline functionality. Command R7B excels at tool use, exhibiting particular strength in using tools in real-world, diverse, and dynamic environments. In addition, Command R7B is good at avoiding unnecessarily calling tools, which is an important aspect of tool-use in practical applications. 
-- Agents - As this is being written, [agents](https://docs.cohere.com/docs/multi-step-tool-use) are among the most exciting frontiers for large language models. Command R7B’s multistep tool use capabilities allow it to power fast and capable REACT agents. When set up as an internet-augmented research agent, for example, Command R7B ably completes tasks that require breaking down complex questions into subgoals, and also performs favorably in domains that utilize complex reasoning and active information seeking. 
-
-Command R7B is available today on the Cohere Platform as well as accessible on HuggingFace, or you can access it in the SDK with `command-r7b-12-2024`. For more information, check out our [dedicated blog post](https://cohere.com/blog/command-r7b).
-
 ## Command R+ August 2024 Release
 Cohere's flagship text-generation models, Command R and Command R+, received a substantial update in August 2024. We chose to designate these models with time stamps, so in the API Command R+ 08-2024 is accesible with `command-r-plus-08-2024`.
 

fern/pages/models/the-command-family-of-models/command-r.mdx

@@ -9,10 +9,9 @@ keywords: >-
   Cohere, large language models, generative AI, command model, chat models,
   conversational AI
 createdAt: 'Tue Mar 05 2024 18:50:03 GMT+0000 (Coordinated Universal Time)'
-updatedAt: 'Mon Jun 10 2024 14:22:50 GMT+0000 (Coordinated Universal Time)'
+updatedAt: 'Wed Dec 18 2024 14:16:00 GMT+0000 (Coordinated Universal Time)'
 ---
 
-
 Command R is a large language model optimized for conversational interaction and long context tasks. It targets the “scalable” category of models that balance high performance with strong accuracy, enabling companies to move beyond proof of concept and into production. 
 
 Command R boasts high precision on [retrieval augmented generation](/docs/retrieval-augmented-generation-rag) (RAG) and tool use tasks, low latency and high throughput, a long 128,000-token context length, and strong capabilities across 10 key languages.
@@ -22,20 +21,10 @@ For information on toxicity, safety, and using this model responsibly check out
 ### Model Details
 | Model Name               | Description                                                                                                                                                                                                                                                                                                                                                | Modality                | Context Length | Maximum Output Tokens | Endpoints|
 |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|----------------|-----------------------|----------|
-| `command-r7b-12-2024`    | `command-r7b-12-2024` is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps.                                                                                                                                                   | Text                    | 128k           | 4k                    | [Chat](/reference/chat)         |
 | `command-r-08-2024`      | `command-r-08-2024` is an update of the Command R model, delivered in August 2024.                                                                                                                                                                                                                                                                         | Text                    | 128k           | 4k                    | [Chat](/reference/chat)         |                                                                          |
 | `command-r-03-2024`      | Command R is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents.                                                         | Text                    | 128k           | 4k                    | [Chat](/reference/chat)         |                                                                         |
 | `command-r`              | `command-r` is an alias for `command-r-03-2024`, so if you use `command-r` in the API, that's the model you're pointing to.                                                                                                                                                                                                                                | Text                    | 128k           | 4k                    | [Chat](/reference/chat)         |                                                                        |
 
-## Command R7B December 2024 Release
-Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused [large language models](https://docs.cohere.com/docs/introduction-to-large-language-models) (LLMs). With a context window of 128K, Command R7B offers state-of-the-art performance across a variety of real-world tasks, and is designed for use cases in which speed, cost, and compute are important. Specifically, Command R7B is excellent for:
-
-- RAG - [Retrieval Augmented Generation](https://docs.cohere.com/docs/retrieval-augmented-generation-rag) (RAG) refers to the practice of ‘grounding’ model outputs in external data sources, which can increase accuracy. Command R7B is exceptionally good at generating responses in conversational tasks, attending over long inputs, and extracting and manipulating numerical information in financial settings.
-- Tool-use - With [tool use](https://docs.cohere.com/docs/tool-use), Command models can be given tools such as search engines, APIs, vector databases, etc., which can expand their baseline functionality. Command R7B excels at tool use, exhibiting particular strength in using tools in real-world, diverse, and dynamic environments. In addition, Command R7B is good at avoiding unnecessarily calling tools, which is an important aspect of tool-use in practical applications. 
-- Agents - As this is being written, [agents](https://docs.cohere.com/docs/multi-step-tool-use) are among the most exciting frontiers for large language models. Command R7B’s multistep tool use capabilities allow it to power fast and capable REACT agents. When set up as an internet-augmented research agent, for example, Command R7B ably completes tasks that require breaking down complex questions into subgoals, and also performs favorably in domains that utilize complex reasoning and active information seeking. 
-
-Command R7B is available today on the Cohere Platform as well as accessible on HuggingFace, or you can access it in the SDK with `command-r7b-12-2024`. For more information, check out our [dedicated blog post](https://cohere.com/blog/command-r7b).
-
 ## Command R August 2024 Release
 Cohere's flagship text-generation models, Command R and Command R+, received a substantial update in August 2024. We chose to designate these models with time stamps, so in the API Command R 08-2024 is accesible with `command-r-08-2024`.
 

fern/pages/models/the-command-family-of-models/command-r7b.mdx

@@ -0,0 +1,28 @@
+---
+title: Command R7B
+subtitle: Cohere's Command R7B model
+slug: docs/command-r7b
+hidden: false
+description: >-
+    Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents.
+image: ../../../assets/images/edb3e49-cohere_meta_image.jpg
+keywords: 'generative AI, Cohere, large language models'
+createdAt: 'Wed Dec 18 2024 14:16:00 GMT+0000 (Coordinated Universal Time)'
+updatedAt: ''
+---
+
+Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused [large language models](https://docs.cohere.com/docs/introduction-to-large-language-models) (LLMs). With a context window of 128K and a compact architecture, Command R7B offers state-of-the-art performance across a variety of real-world tasks, and it is especially good at high throughput, latency-sensitive applications like chatbots and code assistants. What's more, it's small size also unlocks dramatically cheaper deployment infrastructure--such as consumer GPUs and CPUs--which means it can be used for on-device inference.
+
+Command R7B is available today on the Cohere Platform as well as accessible on [HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024), or you can access it in the SDK with `command-r7b-12-2024`. For more information, check out our [dedicated blog post](https://cohere.com/blog/command-r7b).
+
+## Model Details
+| Model Name               | Description                                                                                                                                                                                                                                         | Modality             | Context Length | Maximum Output Tokens | Endpoints              |
+|--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|----------------|-----------------------|------------------------|
+| `command-r7b-12-2024`    | `command-r7b-12-2024` is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps.                                            | Text                 | 128k           | 4k                    | [Chat](/reference/chat)|
+
+## What Can Command R7B Be Used For?
+Command R7B is excellent for:
+
+- RAG - [Retrieval Augmented Generation](https://docs.cohere.com/docs/retrieval-augmented-generation-rag) (RAG) refers to the practice of ‘grounding’ model outputs in external data sources, which can increase accuracy. Command R7B is exceptionally good at generating responses in conversational tasks, attending over long inputs, and extracting and manipulating numerical information in financial settings.
+- Tool-use - With [tool use](https://docs.cohere.com/docs/tool-use), Command models can be given tools such as search engines, APIs, vector databases, etc., which can expand their baseline functionality. Command R7B excels at tool use, exhibiting particular strength in using tools in real-world, diverse, and dynamic environments. In addition, Command R7B is good at avoiding unnecessarily calling tools, which is an important aspect of tool-use in practical applications. 
+- Agents - As this is being written, [agents](https://docs.cohere.com/docs/multi-step-tool-use) are among the most exciting frontiers for large language models. Command R7B’s multistep tool use capabilities allow it to power fast and capable REACT agents. When set up as an internet-augmented research agent, for example, Command R7B ably completes tasks that require breaking down complex questions into subgoals, and also performs favorably in domains that utilize complex reasoning and active information seeking.