Update README.md

README.md

@@ -1,199 +1,371 @@
 ---
+base_model: meta-llama/Meta-Llama-3.1-8B
+language:
+- en
 library_name: transformers
-tags: []
+license: cc-by-nc-4.0
+tags:
+- cohere
+- unsloth
+- transformers
 ---
 
-# Model Card for Model ID
+# Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
 
-<!-- Provide a quick summary of what the model is/does. -->
+We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
 
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
+## ✨ Finetune for Free
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
 
-### Direct Use
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **Llama-3.1 8b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
+| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
+| **Gemma-2 9b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |
+| **Mistral 7b**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
+| **TinyLlama**  | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)              | 3.9x faster | 74% less |
+| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
+- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
+- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
 
-[More Information Needed]
+# Model Card for C4AI Command R+ 08-2024
 
-### Downstream Use [optional]
+## Model Summary
+C4AI Command R+ 08-2024 is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ 08-2024 is a multilingual model trained on 23 languages and evaluated in 10 languages. Command R+ 08-2024 is optimized for a variety of use cases including reasoning, summarization, and question answering.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+C4AI Command R+ 08-2024 is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is [C4AI Command R 08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024).
 
-[More Information Needed]
+- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
+- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
+- Model: c4ai-command-r-plus-08-2024
+- Model Size: 104 billion parameters
+- Context length: 128K
 
-### Out-of-Scope Use
+**Try C4AI Command R+**
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+You can try out C4AI Command R+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/c4ai-command?model=command-r-plus-08-2024).
 
-[More Information Needed]
+**Usage**
 
-## Bias, Risks, and Limitations
+Please use `transformers` version 4.39.1 or higher
+```python
+# pip install 'transformers>=4.39.1'
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+model_id = "CohereForAI/c4ai-command-r-plus-08-2024"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-[More Information Needed]
+# Format message with the command-r-plus-08-2024 chat template
+messages = [{"role": "user", "content": "Hello, how are you?"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
 
-### Recommendations
+gen_tokens = model.generate(
+    input_ids, 
+    max_new_tokens=100, 
+    do_sample=True, 
+    temperature=0.3,
+    )
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+gen_text = tokenizer.decode(gen_tokens[0])
+print(gen_text)
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Model Details
 
-#### Preprocessing [optional]
+**Input**: Models input text only.
 
-[More Information Needed]
+**Output**: Models generate text only.
 
+**Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. We use grouped query attention (GQA) to improve inference speed.
 
-#### Training Hyperparameters
+**Languages covered**: The model has been trained on 23 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian) and evaluated on 10 languages (English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Simplified Chinese).
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+**Context length**: Command R+ 08-2024 supports a context length of 128K.
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Tool use & Agent capabilities:
 
-[More Information Needed]
+Command R+ 08-2024 has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
 
-## Evaluation
+Command R+ 08-2024’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ 08-2024 may use one of its supplied tools more than once.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the `directly_answer` tool, but it can be removed or renamed if required.
 
-### Testing Data, Factors & Metrics
+Comprehensive documentation for working with Command R+ 08-2024's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
 
-#### Testing Data
+Command R+ 08-2024 also supports Hugging Face's [tool use API](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling).
 
-<!-- This should link to a Dataset Card if possible. -->
+The code snippets below show minimal working examples on how to render a prompt.
 
-[More Information Needed]
+<details>
+<summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
 
-#### Factors
+```python
+from transformers import AutoTokenizer
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+model_id = "CohereForAI/c4ai-command-r-plus-08-2024"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-[More Information Needed]
+# define conversation input:
+conversation = [
+    {"role": "user", "content": "Whats the biggest penguin in the world?"}
+]
+# Define tools available for the model to use:
+tools = [
+  {
+    "name": "internet_search",
+    "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
+    "parameter_definitions": {
+      "query": {
+        "description": "Query to search the internet with",
+        "type": 'str',
+        "required": True
+      }
+    }
+  },
+  {
+    'name': "directly_answer",
+    "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
+    'parameter_definitions': {}
+  }
+]
 
-#### Metrics
+# render the tool use prompt as a string:
+tool_use_prompt = tokenizer.apply_tool_use_template(
+    conversation,
+    tools=tools,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print(tool_use_prompt)
+```
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+</details>
 
-[More Information Needed]
 
-### Results
+<details>
+<summary><b>Usage: Rendering prompts with the Tool Use API [CLICK TO EXPAND]</b> </summary>
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer
+
+model_id = "CohereForAI/c4ai-command-r-plus-08-2024"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# define conversation input:
+conversation = [
+    {"role": "user", "content": "Whats the biggest penguin in the world?"}
+]
 
-#### Summary
+# Define tools available for the model to use
+# Type hints and docstrings from Python functions are automatically extracted
+def internet_search(query: str):
+    """
+    Returns a list of relevant document snippets for a textual query retrieved from the internet
 
+    Args:
+        query: Query to search the internet with
+    """
+    pass
 
+def directly_answer():
+    """
+    Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
+    """
+    pass
 
-## Model Examination [optional]
+tools = [internet_search, directly_answer]
 
-<!-- Relevant interpretability work for the model goes here -->
+# render the tool use prompt as a string:
+tool_use_prompt = tokenizer.apply_chat_template(
+    conversation,
+    tools=tools,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print(tool_use_prompt)
+```
+
+</details>
+
+<details>
+<summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary>
+
+````
+<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
+The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
+
+# System Preamble
+## Basic Rules
+You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
+
+# User Preamble
+## Task and Context
+You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
+
+## Style Guide
+Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
+
+## Available Tools
+Here is a list of tools that you have available to you:
+
+```python
+def internet_search(query: str) -> List[Dict]:
+    """Returns a list of relevant document snippets for a textual query retrieved from the internet
+
+    Args:
+        query (str): Query to search the internet with
+    """
+    pass
+```
+
+```python
+def directly_answer() -> List[Dict]:
+    """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
+    """
+    pass
+```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
+```json
+[
+    {
+        "tool_name": title of the tool in the specification,
+        "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
+    }
+]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+````
+
+</details>
+
+
+<details>
+<summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary>
 
-[More Information Needed]
+````
+Action: ```json
+[
+      {
+          "tool_name": "internet_search",
+          "parameters": {
+              "query": "biggest penguin in the world"
+          }
+      }
+]
+```
+````
+</details>
+
+
+### Grounded Generation and RAG Capabilities: 
+
+Command R+ 08-2024 has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
+
+Command R+ 08-2024’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
 
-## Environmental Impact
+By default, Command R+ 08-2024 will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Comprehensive documentation for working with Command R+ 08-2024's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+The code snippet below shows a minimal working example on how to render a prompt.
 
-## Technical Specifications [optional]
+<details>
+<summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>
 
-### Model Architecture and Objective
+````python
+from transformers import AutoTokenizer
 
-[More Information Needed]
+model_id = "CohereForAI/c4ai-command-r-plus-08-2024"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-### Compute Infrastructure
+# define conversation input:
+conversation = [
+    {"role": "user", "content": "Whats the biggest penguin in the world?"}
+]
+# define documents to ground on:
+documents = [
+    { "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." }, 
+    { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
+]
 
-[More Information Needed]
+# render the tool use prompt as a string:
+grounded_generation_prompt = tokenizer.apply_grounded_generation_template(
+    conversation,
+    documents=documents,
+    citation_mode="accurate", # or "fast"
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print(grounded_generation_prompt)
+````
 
-#### Hardware
+</details>
 
-[More Information Needed]
+<details>
+<summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary>
 
-#### Software
+````
+<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
+The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
 
-[More Information Needed]
+# System Preamble
+## Basic Rules
+You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
 
-## Citation [optional]
+# User Preamble
+## Task and Context
+You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Style Guide
+Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>
+Document: 0
+title: Tall penguins
+text: Emperor penguins are the tallest growing up to 122 cm in height.
 
-**BibTeX:**
+Document: 1
+title: Penguin habitats
+text: Emperor penguins only live in Antarctica.
+</results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line.
+Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
+Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'.
+Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup.
+Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+````
 
-[More Information Needed]
+</details>
 
-**APA:**
 
-[More Information Needed]
 
-## Glossary [optional]
+<details>
+<summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary>
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+````
+Relevant Documents: 0,1
+Cited Documents: 0,1
+Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres.
+Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0>
+````
 
-[More Information Needed]
+</details>
 
-## More Information [optional]
 
-[More Information Needed]
+### Code Capabilities:
+Command R+ 08-2024 has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
 
-## Model Card Authors [optional]
+### Model Card Contact
+For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai).
 
-[More Information Needed]
+### Terms of Use: 
+We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
 
-## Model Card Contact
+### Try Chat:
+You can try Command R+ 08-2024 chat in the playground [here](https://dashboard.cohere.com/playground/chat). You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command?model=command-r-plus-08-2024).
 
-[More Information Needed]