transformers safetensors cohere text-generation conversational custom_code en fr de es it pt ja ko zh ar autotrain_compatible text-generation-inference endpoints_compatible 4-bit bitsandbytes region:us
---
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
library_name: transformers
---

# Model Card for C4AI Command-R quantized to 4bit

## Model Summary

This repo contains a 4bit quantized version of [C4AI Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01). 

A 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.

Developed by: Cohere and [Cohere For AI](https://cohere.for.ai)

- 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-v01
- Model Size: 35 billion parameters
- Context length: 128K

**Use**

```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "prince-canuma/c4ai-command-r-v01-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

# Format message with the command-r 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)
```


## Tool use 🛠️

```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "prince-canuma/c4ai-command-r-v01-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


# Format message with the command-r tool use template
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': {}
  }
]

formatted_input = tokenizer.apply_tool_use_template(conversation, tools=tools, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer(formatted_input, return_tensors='pt')['input_ids'].to(device)

outputs = model.generate(
  input_ids, 
  max_new_tokens=100, 
)

print("Output:\n" + 100 * '-')
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```

<details>
<summary><b> Prompt [CLICK TO EXPAND]</b></summary>

````
<|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
```<|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|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
    }
]```<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>

<summary><b> Output </b></summary>

````
Output:
----------------------------------------------------------------------------------------------------
Action:```json
[
    {
        "tool_name": "internet_search",
        "parameters": {
            "query": "biggest penguin in the world"
        }
    }
]
```<|END_OF_TURN_TOKEN|>
````


## RAG use 📚

```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "prince-canuma/c4ai-command-r-v01-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


# Format message with the command-r tool use template
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."}
]


formatted_input = tokenizer.apply_grounded_generation_template(
  conversation,
  documents=documents,
  citation_mode="accurate", # or "fast"
  tokenize=False,
  add_generation_prompt=True,
)
input_ids = tokenizer(formatted_input, return_tensors='pt')['input_ids'].to(device)

outputs = model.generate(
  input_ids, 
  max_new_tokens=100, 
)

print("Output:\n" + 100 * '-')
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```

<details>
<summary><b> Prompt [CLICK TO EXPAND]</b></summary>

````
<BOS_TOKEN><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|>
````
</details>

<summary><b> Output </b></summary>

````
Output:
----------------------------------------------------------------------------------------------------
Relevant Documents: 0,1
Cited Documents: 0
Answer: The tallest species of penguin in the world is the emperor penguin (Aptenodytes forsteri), which can reach heights of up to 122 cm.
Grounded answer: The tallest species of penguin in the world is the <co: 0>emperor penguin</co: 0> <co: 0>(Aptenodytes forsteri)</co: 0>, which can reach <co: 0>heights of up to 122 cm.</co: 0><|END_OF_TURN_TOKEN|>
````
  
## 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.

**Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic. 

Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.

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

### Code Capabilities:
Command-R 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 [info@for.ai](mailto:info@for.ai).

### 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 35 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).

### Try Chat:
You can try Command-R chat in the playground [here](https://dashboard.cohere.com/playground/chat).