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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,300 @@
----
-license: cc0-1.0
----
+---
+library_name: transformers
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ja
+- ko
+- zh
+- ar
+license: cc-by-nc-4.0
+---
+
+# Model Card for C4AI Command-R
+
+🚨 **This model is 4bit quantized version of C4AI Command-R using bitsandbytes.** You can find the unquantized version of C4AI Command-R [here](https://huggingface.co/CohereForAI/c4ai-command-r-v01).
+
+## Model Summary
+
+C4AI Command-R is a research release of 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
+
+**Usage**
+
+Please use `transformers` version 4.39.1 or higher
+```python
+# pip install 'transformers>=4.39.1' bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "CohereForAI/c4ai-command-r-v01-4bit"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# 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)
+```
+
+## 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.
+
+### Tool use capabilities:
+
+Command-R 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.
+
+Command-R’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 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's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
+
+The code snippet below shows a minimal working example on how to render a prompt.
+
+<details>
+<summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
+
+```python
+from transformers import AutoTokenizer
+
+model_id = "CohereForAI/c4ai-command-r-v01"
+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)
+```
+
+</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>
+
+````
+Action: ```json
+[
+      {
+          "tool_name": "internet_search",
+          "parameters": {
+              "query": "biggest penguin in the world"
+          }
+      }
+]
+```
+````
+</details>
+
+### Grounded Generation and RAG Capabilities: 
+
+Command-R 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’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble), 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.
+
+Command-R’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.
+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's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
+
+The code snippet below shows a minimal working example on how to render a prompt.
+
+<details>
+<summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>
+
+````python
+from transformers import AutoTokenizer
+
+model_id = "CohereForAI/c4ai-command-r-v01"
+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)
+````
+</details>
+
+<details>
+<summary><b>Example Rendered Grounded Generation 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.<|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>
+
+<details>
+<summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary>
+
+````
+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>
+````
+</details>
+
+### 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).

config.json

@@ -0,0 +1,48 @@
+{
+  "_name_or_path": "CohereForAI/c4ai-command-r-v01",
+  "architectures": [
+    "CohereForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "CohereForAI/c4ai-command-r-v01--configuration_cohere.CohereConfig",
+    "AutoModel": "CohereForAI/c4ai-command-r-v01--modeling_cohere.CohereModel",
+    "AutoModelForCausalLM": "CohereForAI/c4ai-command-r-v01--modeling_cohere.CohereForCausalLM"
+  },
+  "bos_token_id": 5,
+  "eos_token_id": 255001,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 22528,
+  "layer_norm_eps": 1e-05,
+  "logit_scale": 0.0625,
+  "max_position_embeddings": 8192,
+  "model_type": "cohere",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 64,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "float16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "fp4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rope_theta": 8000000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.0.dev0",
+  "use_cache": true,
+  "vocab_size": 256000
+}

configuration_cohere.py

@@ -0,0 +1,159 @@
+# coding=utf-8
+# Copyright 2024 Cohere team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Cohere model configuration"""
+
+from transformers import PretrainedConfig, AutoConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class CohereConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
+    model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 256000):
+            Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`CohereModel`]
+        hidden_size (`int`, *optional*, defaults to 8192):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22528):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 40):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with. 
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 5):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 255001):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import CohereModel, CohereConfig
+
+    >>> # Initializing a Cohere model configuration
+    >>> configuration = CohereConfig()
+
+    >>> # Initializing a model from the Cohere configuration
+    >>> model = CohereModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "cohere"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=256000,
+        hidden_size=8192,
+        intermediate_size=22528,
+        num_hidden_layers=40,
+        num_attention_heads=64,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=5,
+        eos_token_id=255001,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+# register the model config to AutoConfig
+AutoConfig.register("cohere", CohereConfig)

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 5,
+  "eos_token_id": 255001,
+  "pad_token_id": 0,
+  "transformers_version": "4.39.0.dev0"
+}

model-00001-of-00005.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<BOS_TOKEN>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|END_OF_TURN_TOKEN|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_cohere_fast.py

@@ -0,0 +1,754 @@
+# coding=utf-8
+# Copyright 2024 Cohere and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is based on the tokenization_llama_fast.py file in transformers
+
+
+import os
+from shutil import copyfile
+from typing import Optional, Tuple, Dict, Union, List, Literal
+
+from tokenizers import processors
+from transformers import AutoTokenizer
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import logging
+from transformers.utils.versions import require_version
+from transformers.tokenization_utils_base import TensorType
+from transformers.pipelines.conversational import Conversation
+
+from .configuration_cohere import CohereConfig
+
+require_version("tokenizers>=0.13.3")
+
+logger = logging.get_logger(__name__)
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "cohere-tokenizer": "https://huggingface.co/Cohere/Command-nightly/blob/main/tokenizer.json",
+    },
+}
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = "You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere."
+DEFAULT_RAG_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."""
+# fmt: on
+
+
+class CohereTokenizerFast(PreTrainedTokenizerFast):
+    """
+    Construct a Cohere tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    This uses notably ByteFallback and NFC normalization.
+
+    ```python
+    >>> from transformers import AutoTokenizer
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-0.1")
+    >>> tokenizer.encode("Hello this is a test")
+    [1, 15043, 445, 338, 263, 1243]
+    ```
+
+    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
+    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
+    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
+    [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
+
+
+    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+    refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`, *optional*):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        tokenizer_file (`str`, *optional*):
+            [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
+            contains everything needed to load the tokenizer.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Cohere tokenizer should be used.
+        add_prefix_space (`bool`, *optional*):
+            Whether or not the tokenizer should automatically add a prefix space
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    padding_side = "left"
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        clean_up_tokenization_spaces=False,
+        unk_token="<UNK>",
+        bos_token="<BOS_TOKEN>",
+        eos_token="<EOS_TOKEN>",
+        add_bos_token=True,
+        add_eos_token=False,
+        use_default_system_prompt=False,
+        add_prefix_space=None,
+        **kwargs,
+    ):
+        if add_prefix_space is not None:
+            logger.warning_once(
+                "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers"
+            )
+            kwargs["from_slow"] = True
+
+        super().__init__(
+            vocab_file=vocab_file,
+            tokenizer_file=tokenizer_file,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            use_default_system_prompt=use_default_system_prompt,
+            **kwargs,
+        )
+        self._add_bos_token = add_bos_token
+        self._add_eos_token = add_eos_token
+        self.update_post_processor()
+        self.use_default_system_prompt = use_default_system_prompt
+        self.vocab_file = vocab_file
+        self.grounded_generation_template = kwargs.pop("grounded_generation_template", None)
+        self.tool_use_template = kwargs.pop("tool_use_template", None)
+
+    def update_post_processor(self):
+        """
+        Updates the underlying post processor with the current `bos_token` and `eos_token`.
+        """
+        bos = self.bos_token
+        bos_token_id = self.bos_token_id
+        if bos is None and self.add_bos_token:
+            raise ValueError("add_bos_token = True but bos_token = None")
+
+        eos = self.eos_token
+        eos_token_id = self.eos_token_id
+        if eos is None and self.add_eos_token:
+            raise ValueError("add_eos_token = True but eos_token = None")
+
+        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
+        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
+
+        special_tokens = []
+        if self.add_bos_token:
+            special_tokens.append((bos, bos_token_id))
+        if self.add_eos_token:
+            special_tokens.append((eos, eos_token_id))
+        self._tokenizer.post_processor = processors.TemplateProcessing(
+            single=single, pair=pair, special_tokens=special_tokens
+        )
+
+    @property
+    def add_eos_token(self):
+        return self._add_eos_token
+
+    @property
+    def add_bos_token(self):
+        return self._add_bos_token
+
+    @add_eos_token.setter
+    def add_eos_token(self, value):
+        self._add_eos_token = value
+        self.update_post_processor()
+
+    @add_bos_token.setter
+    def add_bos_token(self, value):
+        self._add_bos_token = value
+        self.update_post_processor()
+
+    @property
+    def default_chat_template(self):
+        """
+        Cohere Tokenizer uses <|START_OF_TURN_TOKEN|> and <|END_OF_TURN_TOKEN|> to indicate each turn in a chat. 
+        Additioanlly, to indicate the source of the message, <|USER_TOKEN|>, <|CHATBOT_TOKEN|> and <|SYSTEM_TOKEN|> 
+        for user, assitant and system messages respectively.
+
+        The output should look something like:
+        <|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ preamble }}<|END_OF_TURN_TOKEN|>
+        <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ How are you? }}<|END_OF_TURN_TOKEN|>
+        <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{{ I am doing well! }}<|END_OF_TURN_TOKEN|>
+
+        Use add_generation_prompt to add a prompt for the model to generate a response:
+
+        >>> messages = [{"role": "user", "content": "Hello, how are you?"}]
+        >>> tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+
+        """
+        logger.warning_once(
+            "\nNo chat template is defined for this tokenizer - using the default template "
+            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
+            "your model, please set `tokenizer.chat_template` to an appropriate template. "
+            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
+        )
+        template = (
+            "{{ bos_token }}"
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true %}"
+            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = false %}"
+            "{% endif %}"
+            "{% if system_message != false %}"  # Start with system message
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% endif %}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
+            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
+            "{% endif %}"
+            "{% set content = message['content'] %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% endif %}"
+            "{% endfor %}"
+            "{% if add_generation_prompt %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}"
+            "{% endif %}"
+        )
+        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
+        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+
+        return template
+
+    @property
+    def default_tool_use_template(self):
+        template = (
+            "{{ bos_token }}"    
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% endif %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }}"
+            "{{ '# Safety Preamble' }}"
+            "{{ '\nThe instructions in this section override those in the task description and style guide sections. Don\\'t answer questions that are harmful or immoral.' }}"
+            "{{ '\n\n# System Preamble' }}"
+            "{{ '\n## Basic Rules' }}"
+            "{{ '\nYou 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.' }}"
+            "{{ '\n\n# User Preamble' }}"
+            "{{ '\n' + system_message }}" 
+            "{{'\n\n## Available Tools\nHere is a list of tools that you have available to you:\n\n'}}"
+            "{% for tool in tools %}"
+            "{% if loop.index0 != 0 %}"
+            "{{ '\n\n'}}"
+            "{% endif %}"
+            "{{'```python\ndef ' + tool.name + '('}}"
+            "{% for param_name, param_fields in tool.parameter_definitions.items() %}"
+            "{% if loop.index0 != 0 %}"
+            "{{ ', '}}"
+            "{% endif %}"
+            "{{param_name}}: "
+            "{% if not param_fields.required %}"
+            "{{'Optional[' + param_fields.type + '] = None'}}"
+            "{% else %}"
+            "{{ param_fields.type }}"
+            "{% endif %}"
+            "{% endfor %}" 
+            "{{ ') -> List[Dict]:\n    \"\"\"'}}"
+            "{{ tool.description }}"
+            "{% if tool.parameter_definitions|length != 0 %}"
+            "{{ '\n\n    Args:\n        '}}"
+            "{% for param_name, param_fields in tool.parameter_definitions.items() %}"
+            "{% if loop.index0 != 0 %}"
+            "{{ '\n        ' }}"
+            "{% endif %}"
+            "{{ param_name + ' ('}}"
+            "{% if not param_fields.required %}"
+            "{{'Optional[' + param_fields.type + ']'}}"
+            "{% else %}"
+            "{{ param_fields.type }}"
+            "{% endif %}"
+            "{{ '): ' + param_fields.description }}"
+            "{% endfor %}" 
+            "{% endif %}"
+            "{{ '\n    \"\"\"\n    pass\n```' }}"
+            "{% endfor %}"
+            "{{ '<|END_OF_TURN_TOKEN|>'}}"
+            "{% for message in loop_messages %}"
+            "{% set content = message['content'] %}"
+            "{% if message['role'] == 'user' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% endif %}"
+            "{% endfor %}"
+            "{{'<|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:\n```json\n[\n    {\n        \"tool_name\": title of the tool in the specification,\n        \"parameters\": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters\n    }\n]```<|END_OF_TURN_TOKEN|>'}}"
+            "{% if add_generation_prompt %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}"
+            "{% endif %}"
+        )
+        default_message = DEFAULT_RAG_PREAMBLE.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+        return template
+
+    @property
+    def default_grounded_generation_template(self):
+        template = (
+            "{{ bos_token }}"    
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% endif %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }}"
+            "{{ '# Safety Preamble' }}"
+            "{{ '\nThe instructions in this section override those in the task description and style guide sections. Don\\'t answer questions that are harmful or immoral.' }}"
+            "{{ '\n\n# System Preamble' }}"
+            "{{ '\n## Basic Rules' }}"
+            "{{ '\nYou 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.' }}"
+            "{{ '\n\n# User Preamble' }}"
+            "{{ '\n' + system_message }}"
+            "{{ '<|END_OF_TURN_TOKEN|>'}}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% set content = message['content'] %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}"
+            "{% endif %}"
+            "{% endfor %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>'}}"
+            "{{ '<results>' }}"
+            "{% for document in documents %}"  # Loop over all non-system messages
+            "{{ '\nDocument: ' }}"
+            "{{ loop.index0 }}\n"
+            "{% for key, value in document.items() %}"
+            "{{ key }}: {{value}}\n"
+            "{% endfor %}"
+            "{% endfor %}"
+            "{{ '</results>'}}"
+            "{{ '<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }}"
+            "{{ 'Carefully perform the following instructions, in order, starting each with a new line.\n' }}"
+            "{{ '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\\'.\n' }}"
+            "{{ '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\\'.\n' }}"
+            "{% if citation_mode=='accurate' %}"
+                "{{ '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.\n' }}"
+            "{% endif %}"
+            "{{ '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|>' }}"
+            "{% if add_generation_prompt %}"
+            "{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}"
+            "{% endif %}"
+        )
+        default_message = DEFAULT_RAG_PREAMBLE.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+        return template
+
+    def _apply_template_with_arguments(
+        self,
+        conversation: Union[List[Dict[str, str]], "Conversation"],
+        template: Optional[str] = None,
+        add_generation_prompt: bool = False,
+        tokenize: bool = True,
+        padding: bool = False,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_dict: bool = False,
+        **kwargs,
+    ) -> Union[str, List[int]]:
+        """Just tokenization_utils_base.apply_chat_template, but modified so that the jinjia template can take kwargs"""
+        if hasattr(conversation, "messages"):
+            # Indicates it's a Conversation object
+            conversation = conversation.messages
+
+        # Compilation function uses a cache to avoid recompiling the same template
+        compiled_template = self._compile_jinja_template(template)
+
+        rendered = compiled_template.render(
+            messages=conversation,
+            add_generation_prompt=add_generation_prompt,
+            **kwargs,
+            **self.special_tokens_map
+        )
+
+        if padding is True:
+            padding = "max_length"  # There's only one sequence here, so "longest" makes no sense
+        if tokenize:
+            if return_dict:
+                return self(
+                    rendered,
+                    padding=padding,
+                    truncation=truncation,
+                    max_length=max_length,
+                    add_special_tokens=False,
+                    return_tensors=return_tensors,
+                    **kwargs,
+                )
+            else:
+                return self.encode(
+                    rendered,
+                    padding=padding,
+                    truncation=truncation,
+                    max_length=max_length,
+                    add_special_tokens=False,
+                    return_tensors=return_tensors,
+                    **kwargs,
+                )
+        else:
+            return rendered
+
+    def apply_tool_use_template(
+        self,
+        conversation: Union[List[Dict[str, str]], "Conversation"],
+        tools: List[Dict],
+        tool_use_template: Optional[str] = None,
+        **kwargs
+    ) -> Union[str, List[int]]:
+        """Create a Command-R tool-use prompt.
+
+        Once rendered, the prompt instructs the model to generate a list of actions to perform on a set of user supplied tools 
+        to help carry out the user's requests.
+
+        Conceptually, this works in the same way as `apply_chat_format`, but takes an additional `tools` parameter.
+
+        Converts a Conversation object or a list of dictionaries with `"role"` and `"content"` keys and a list of available
+        tools for the model to use into a prompt string, or a list of token ids. 
+        This method will use the tokenizer's `default_tool_use_template` template specified at the class level.
+        You can override the default template using the `tool_use_template` kwarg but the quality of your results may decrease.
+
+        Args:
+            conversation (Union[List[Dict[str, str]], "Conversation"]): A Conversation object or list of dicts
+                with "role" and "content" keys, representing the chat history so far.
+            tools (List[Dict]): a list of tools to render into the prompt for the model to choose from.
+                See an example at the bottom of the docstring.
+                The format should be:
+                   * name (str): The name of the tool to be called. Valid names contain only the characters a-z,
+                        A-Z, 0-9, _ and must not begin with a digit.
+                   * description (str): The description of what the tool does, the model uses the description to 
+                        choose when and how to call the function.
+                   * parameter_definitions (List[Dict]): The input parameters of the tool. Accepts a dictionary 
+                        where the key is the name of the parameter and the value is the parameter spec.
+                        Valid parameter names contain only the characters a-z, A-Z, 0-9, _ and must not begin with a digit. 
+                        Parameter specs are as follows:
+                       * description (str): The description of the parameter.
+                       * type (str): the type of the parameter - most effective for python builtin data types, such as 'str', 'bool'
+                       * required: boolean: Denotes whether the parameter is always present (required) or not. Defaults to not required.
+            tool_use_template (str, *optional*): A Jinja template to use for this conversion. If
+                this is not passed, the model's default chat template will be used instead.
+            add_generation_prompt (bool, *optional*): Whether to end the prompt with the token(s) that indicate
+                the start of an assistant message. This is useful when you want to generate a response from the model.
+                Note that this argument will be passed to the chat template, and so it must be supported in the
+                template for this argument to have any effect.
+            tokenize (`bool`, defaults to `True`):
+                Whether to tokenize the output. If `False`, the output will be a string.
+            padding (`bool`, defaults to `False`):
+                Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`.
+            truncation (`bool`, defaults to `False`):
+                Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.
+            max_length (`int`, *optional*):
+                Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If
+                not specified, the tokenizer's `max_length` attribute will be used as a default.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable
+                values are:
+                - `'tf'`: Return TensorFlow `tf.Tensor` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+            return_dict (`bool`, *optional*, defaults to `False`):
+                Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
+            **tokenizer_kwargs: Additional kwargs to pass to the tokenizer.
+
+        Returns:
+            `str`: A rendered prompt string.
+            or if tokenize=True:
+            `List[int]`: A list of token ids representing the tokenized chat so far, including control tokens. This
+            output is ready to pass to the model, either directly or via methods like `generate()`.
+        
+        Examples:
+
+        ```python
+        >>> tokenizer = CohereTokenizerFast.from_pretrained("CohereForAI/c4ai-command-r-0.1")
+        >>> 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": {}
+            }
+        ]
+        >>> conversation = [
+            {"role": "user", "content": "Whats the biggest penguin in the world?"}
+        ]
+        >>> # render the prompt, ready for user to inspect, or for input into the model:
+        >>> prompt = tokenizer.apply_tool_use_template(
+            conversation,
+            tools=tools,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        >>> print(prompt)
+        <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|>
+        ```
+        >>> inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
+        >>> outputs = model.generate(inputs, max_new_tokens=128)
+        >>> print(tokenizer.decode(outputs[0]))
+        Action: ```json
+        [
+            {
+                "tool_name": "internet_search",
+                "parameters": {
+                    "query": "biggest penguin in the world"
+                }
+            }
+        ]
+        ```
+        """
+        # priority: `tool_use_template` argument > `tokenizer.tool_use_template` > `tokenizer.default_tool_use_template`
+        if tool_use_template is None:
+            if self.tool_use_template is not None:
+                tool_use_template = self.tool_use_template
+            else:  
+                tool_use_template = self.default_tool_use_template
+        
+        return self._apply_template_with_arguments(
+            conversation,
+            tools=tools,
+            template=tool_use_template,
+            **kwargs,
+        )
+
+    def apply_grounded_generation_template(
+        self,
+        conversation: Union[List[Dict[str, str]], "Conversation"],
+        documents: List[Dict],
+        citation_mode: Literal["fast", "accurate"] = "accurate",
+        grounded_generation_template: Optional[str] = None,
+        **kwargs
+    ) -> Union[str, List[int]]:
+        """Create a Command-R grounded generation (aka RAG) prompt.
+
+        Once rendered, the prompt instructs the model to generate a response with citations in, based on supplied documents.
+
+        Conceptually, this works in the same way as `apply_chat_format`, but takes additional `documents`
+        and parameter `citation_mode` parameters.
+
+        Converts a Conversation object or a list of dictionaries with `"role"` and `"content"` keys and a list of 
+        documents for the model to ground its response on into a prompt string, or a list of token ids. 
+        This method will use the tokenizer's `grounded_generation_template` template specified at the class level.
+        You can override the default template using the `grounded_generation_template` kwarg but the quality of your results may decrease.
+
+        Args:
+            conversation (Union[List[Dict[str, str]], "Conversation"]): A Conversation object or list of dicts
+                with "role" and "content" keys, representing the chat history so far.
+            documents (List[Dict[str, str]): A list of dicts, representing documents or tool outputs to ground your 
+                generation on. A document is a semistructured dict, wiht a string to string mapping. Common fields are
+                `url`, `title`, `snippet` etc but should be descriptive of the key. They will get rendered into the prompt.
+            citation_mode: either "accurate" (prompt the model to generate an answer first, then rewrite it with citation 
+                spans in) or "fast", where the prompt instructs the model to generate an answer with citations in directly.
+                The former has higher quality citations, the latter requires fewer tokens to be generated.
+            grounded_generation_template (str, *optional*): A Jinja template to use for this conversion. If
+                this is not passed, the model's default grounded_generation_template template will be used instead.
+            add_generation_prompt (bool, *optional*): Whether to end the prompt with the token(s) that indicate
+                the start of an assistant message. This is useful when you want to generate a response from the model.
+                Note that this argument will be passed to the chat template, and so it must be supported in the
+                template for this argument to have any effect.
+            tokenize (`bool`, defaults to `True`):
+                Whether to tokenize the output. If `False`, the output will be a string.
+            padding (`bool`, defaults to `False`):
+                Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`.
+            truncation (`bool`, defaults to `False`):
+                Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.
+            max_length (`int`, *optional*):
+                Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If
+                not specified, the tokenizer's `max_length` attribute will be used as a default.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable
+                values are:
+                - `'tf'`: Return TensorFlow `tf.Tensor` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+            return_dict (`bool`, *optional*, defaults to `False`):
+                Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
+            **tokenizer_kwargs: Additional kwargs to pass to the tokenizer.
+
+        Returns:
+            `str`: A rendered prompt string.
+            or if tokenize=True:
+            `List[int]`: A list of token ids representing the tokenized chat so far, including control tokens. This
+            output is ready to pass to the model, either directly or via methods like `generate()`.
+        
+        Examples:
+
+        ```python
+        >>> tokenizer = CohereTokenizerFast.from_pretrained('CohereForAI/c4ai-command-r-0.1')
+        
+        >>> # define documents:
+        >>> documents = [
+            { "title": "Tall penguins", "text": "Emperor penguins are the tallest." }, 
+            { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
+        ]
+        >>> # define a conversation:
+        >>> conversation = [
+            {"role": "user", "content": "Whats the biggest penguin in the world?"}
+        ]
+        >>> # render the prompt, ready for user to inspect, or for input into the model:
+        >>> grounded_generation_prompt = tokenizer.apply_grounded_generation_template(
+            conversation,
+            documents=documents,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        >>> print(grounded_generation_prompt)
+        <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.
+ 
+        ## 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.
+
+        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|>'''
+        ```
+        >>> inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
+        >>> outputs = model.generate(inputs, max_new_tokens=128)
+        >>> print(tokenizer.decode(outputs[0]))
+        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>
+        """
+        # priority: `grounded_generation_template` argument > `tokenizer.grounded_generation_template` > `tokenizer.default_grounded_generation_template`
+        if grounded_generation_template is None:
+            if self.grounded_generation_template is not None:
+                grounded_generation_template = self.grounded_generation_template
+            else:  
+                grounded_generation_template = self.default_grounded_generation_template
+        
+        return self._apply_template_with_arguments(
+            conversation,
+            documents=documents,
+            template=grounded_generation_template,
+            citation_mode=citation_mode,
+            **kwargs,
+        )
+
+    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+
+# register the tokenizer to AutoTokenizer
+AutoTokenizer.register(CohereConfig, fast_tokenizer_class=CohereTokenizerFast)

tokenizer.json

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+oid sha256:0cc8a79eafcf1043fbfad77df083de446a61424b222284d602c4edee497ce1e4
+size 12777405

tokenizer_config.json

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+    "add_eos_token": false,
+    "added_tokens_decoder": {
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+      },
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