Cohere Labs Command R 08-2024 is a research release of a 32 billion parameter highly performant generative model. Command R 08-2024 is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command R 08-2024 has the capability for multilingual generation, trained on 23 languages and evaluated in 10 languages and highly performant RAG capabilities.

Developed by: Cohere and Cohere Labs

Point of Contact: Cohere Labs
License: CC-BY-NC, requires also adhering to Cohere Lab's Acceptable Use Policy
Model: c4ai-command-r-08-2024
Model Size: 32 billion parameters
Context length: 128K

Try Cohere Labs Command R

If you want to try Command R before downloading the weights, the model is hosted in a hugging face space here.

Usage

Please use transformers version 4.39.1 or higher

python

# pip install 'transformers>=4.39.1'
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "CohereLabs/c4ai-command-r-08-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Format message with the command-r-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|>

gen_tokens = model.generate(
    input_ids, 
    max_new_tokens=100, 
    do_sample=True, 
    temperature=0.3,
)

gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)

Model Details

Input: Models input text only.

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.

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).

Context length: Command R 08-2024 supports a context length of 128K.

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.. This is why we recommend using the prompt template described below.

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.

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.

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.

Comprehensive documentation for working with Command R 08-2024's grounded generation prompt template can be found here, here and here.

You can render the Grounded Generation prompt template by using the function apply_grounded_generation_template(). The code snippet below shows a minimal working example on how to render this prompt.

Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]

python

from transformers import AutoTokenizer

model_id = "CohereLabs/c4ai-command-r-08-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 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."}
]

# 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)

Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]

<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.<|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.

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|>

Example Rendered Grounded Generation Completion [CLICK TO EXPAND]

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>

Single-Step Tool Use Capabilities ("Function Calling"):

Single-step tool use (or “Function Calling”) allows Command R 08-2024 to interact with external tools like APIs, databases, or search engines. Single-step tool use is made of two model inferences:

Tool Selection: The model decides which tools to call and with what parameters. It’s then up to the developer to execute these tool calls and obtain tool results.
Response Generation: The model generates the final response given the tool results. You can learn more about single step tool use in our documentation.

Command R 08-2024 has been specifically trained with single-step tool use (or “Function Calling”) 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 may reduce performance. This is why we recommend using the prompt template described below.

Command R 08-2024’s single-step 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.

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.

Comprehensive documentation for working with Command R 08-2024's single-step tool use prompt template can be found here and here.

You can render the single-step tool use prompt template by using the function apply_tool_use_template(). The code snippet below shows a minimal working example on how to render this prompt.

Command R 08-2024 also supports Hugging Face's tool use API to render the same prompt.

Usage: Rendering Single-Step Tool Use Prompts [CLICK TO EXPAND]

python

from transformers import AutoTokenizer

model_id = "CohereLabs/c4ai-command-r-08-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 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': {}
  }
]

# 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)

Usage: Rendering prompts with the Single-Step Tool Use API [CLICK TO EXPAND]

python

from transformers import AutoTokenizer

model_id = "CohereLabs/c4ai-command-r-08-2024"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# define conversation input:
conversation = [
    {"role": "user", "content": "Whats the biggest penguin in the world?"}
]

# 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

tools = [internet_search, directly_answer]

# 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)

Example Rendered Single-Step Tool Use Prompt [CLICK TO EXPAND]

<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|>

Example Rendered Single-Step Tool Use Completion [CLICK TO EXPAND]

Action: ```json
[
      {
          "tool_name": "internet_search",
          "parameters": {
              "query": "biggest penguin in the world"
          }
      }
]
```

Multi-Step Tool Use Capabilities ("Agents"):

Multi-step tool use is suited for building agents that can plan and execute a sequence of actions using multiple tools. Unlike single-step tool use, the model can perform several inference cycles, iterating through Action → Observation → Reflection until it decides on a final response. For more details, refer to our documentation on multi-step tool use.

Command R 08-2024 has been specifically trained with multi-step tool use (or “Agents”) 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 may reduce performance. This is why we recommend using the prompt template described below.

The prompt template is not yet available in HuggingFace. However, comprehensive documentation for working with Command R 08-2024's multi-step tool use prompt template can be found here and here.

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 Contact

For errors or additional questions about details in this model card, contact labs@cohere.com

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 32 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to Cohere Lab's Acceptable Use Policy.

Try Chat:

You can try Command-R chat in the playground here.

Cite

To cite this model, use:

@misc {cohere_for_ai_2024,
	author       = { {Cohere Labs} },
	title        = { c4ai-command-r-08-2024 },
	year         = 2024,
	url          = { https://huggingface.co/CohereLabs/c4ai-command-r-08-2024 },
	doi          = { 10.57967/hf/3134 },
	publisher    = { Hugging Face }
}

MetaphoricalCode/c4ai-command-r-08-2024-exl3-4.65bpw-hb6

Quantized using the default exllamav3 (0.0.2) quantization process.

Model Card for C4AI Command R 08-2024

Model Summary

Model Details

Grounded Generation and RAG Capabilities:

Single-Step Tool Use Capabilities ("Function Calling"):

Multi-Step Tool Use Capabilities ("Agents"):

Code Capabilities:

Model Card Contact

Terms of Use:

Try Chat:

Cite

MetaphoricalCode/
c4ai-command-r-08-2024-exl3-4.65bpw-hb6