Copy model from CohereLabs

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README.md

@@ -0,0 +1,191 @@
+---
+inference: false
+library_name: transformers
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ja
+- ko
+- zh
+- ar
+- el
+- fa
+- pl
+- id
+- cs
+- he
+- hi
+- nl
+- ro
+- ru
+- tr
+- uk
+- vi
+license: cc-by-nc-4.0
+extra_gated_prompt: "By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license)  and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll receive email updates about Cohere Labs and Cohere research, events, products and services. You can unsubscribe at any time." 
+extra_gated_fields:
+ Name: text
+ Affiliation: text
+ Country: country
+ I agree to use this model for non-commercial use ONLY: checkbox
+---
+
+# **Model Card for Cohere Labs Command R7B Arabic**
+
+## **Model Summary**
+
+Cohere Labs Command R7B Arabic is an open weights research release of a 7 billion parameter custom model with advanced capabilities optimized for the Arabic language (MSA dialect) along with English. The model excels at tasks that enterprises care about: instruction following, length control, RAG, and responding in the correct language. It also demonstrates excellent general purpose knowledge and understanding of Arabic language and cultures.
+
+Developed by [Cohere](https://cohere.com/) and [Cohere Labs](https://cohere.for.ai/).
+
+* Point of Contact: [Cohere Labs](https://cohere.for.ai/)  
+* License: [CC-BY-NC](https://cohere.com/cohere-labs-cc-by-nc-license), requires also adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy) 
+* Model: c4ai-command-r7b-arabic-02-2025  
+* Model Size: \~8 billion parameters (7 billion transformer parameters \+ 1 billion embedding parameters)
+* Context length: 128K
+
+**Model Performance** 
+
+Cohere Labs Command R7B Arabic excels on standardized and externally verifiable Arabic language benchmarks such as AlGhafa-Native, Arabic MMLU, instruction following (IFEval Arabic), and RAG (TyDi QA Arabic and FaithEval Arabic\*).
+
+| Model | C4AI Command R7B Arabic | Command R7B | Gemma 9B | Llama 3.1 8B | Qwen 2.5 7B | Ministral 8B |
+| :---- | ----- | ----- | ----- | ----- | ----- | ----- |
+| **Average** | **69.3** | 65.8 | 67.0 | 58.4 | 62.9 | 52.5 |
+| AlGhafa-Native | **82.2** | 81.5 | 81.3 | 80.1 | 80.2 | 76.6 |
+| Arabic MMLU | 60.9 | 59.7 | 62.4 | 56.6 | 61.2 | 53.6 |
+| IFEval AR | **69.0** | 57.8 | 67.8 | 48.4 | 62.4 | 49.3 |
+| TyDI QA Arabic | **83.0** | 79.9 | 76.4 | 65.9 | 60.9 | 57.7 |
+| FaithEval Arabic\* | **51.6** | 49.9 | 47.0 | 40.9 | 49.9 | 25.5 |
+
+\* FaithEval Arabic has been professionally translated from English to Arabic based on the well-known RAG benchmark ([https://github.com/SalesforceAIResearch/FaithEval](https://github.com/SalesforceAIResearch/FaithEval)).
+
+Cohere Labs Command R7B Arabic excels on standardized and externally verifiable benchmarks such as the [HuggingFace Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/).
+
+|  | C4AI Command R7B Arabic | Command R7B | Gemma 9B | Llama 3.1 8B | Qwen 2.5 7B | Ministral 8B |
+| :---- | :---- | :---- | :---- | :---- | :---- | :---- |
+| **Average** | 31.4 | 31.6 | 32.1 | 28.2 | 35.2 | 22.0 |
+| IfEval | **83.3** | 77.1 | 74.4 | 78.6 | 75.9 | 59.0 |
+| BBH | 36.2 | 36.0 | **42.1** | 29.9 | 34.9 | 25.8 |
+| MuSR | **11.9** | 10.2 | 9.7 | 8.4 | 8.5 | 8.4 |
+| GPQA | 7.9 | 7.8 | **14.8** | 2.4 | 5.5 | 4.5 |
+| MMLU Pro | 29.4 | 28.6 | **32.0** | 30.7 | 36.5 | 30.7 |
+| MATH\* | 19.6 | 29.9 | 19.1 | 19.3 | 50.0 | 19.6 |
+
+\* The MATH benchmark used in this leaderboard changed in early January due to a DMCA takedown notice for the original benchmark. 
+
+**Try Command R7B Arabic**
+
+You can try out Cohere Labs Command R7B Arabic in our hosted [Hugging Face Space](https://coherelabs-c4ai-command.hf.space/models/command-r7b-arabic-02-2025) before downloading the weights.
+
+**Usage**
+
+Please install transformers from the source repository that includes the necessary changes for this model.
+
+```py
+# pip install 'git+https://github.com/huggingface/transformers.git'
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "CohereLabs/c4ai-command-r7b-arabic-02-2025"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Format message with the c4ai-command-r7b-arabic-02-2025 chat template
+messages = [{"role": "user", "content": "مرحبا، كيف حالك؟"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+
+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. The model features three layers with **sliding window attention** (window size 4096\) and **ROPE** for efficient local context modeling and relative positional encoding. A fourth layer uses **global attention** without positional embeddings, enabling unrestricted token interactions across the entire sequence. 
+
+**Languages covered**: The model has been trained and evaluated for performance in Arabic and English, but its training data includes samples from other languages.
+
+**Context length**: Command R7B Arabic supports a context length of 128,000 tokens.
+
+### **Chat Capabilities:**
+
+Command R7B Arabic can be configured as both a conversational and instruct model based on which preamble is supplied.
+
+The conversational mode conditions the model on interactive behavior, meaning it’s expected to reply conversationally, provide introductory statements and follow-up questions, and use Markdown as well as LaTeX where appropriate. It is optimized for interactive experiences, such as chatbots, where the model engages in dialogue.
+
+The instruct mode, by contrast, conditions the model to provide concise yet comprehensive responses and does not use Markdown / LaTeX by default. It is designed for non-interactive, task-focused use cases such as extracting information, summarizing text, translation, and categorization.
+
+**Note:** Command R7B Arabic is delivered without a system preamble by default, though we encourage you to experiment with the conversational and instruct mode preambles. More information can be found in our [docs](https://docs.cohere.com/docs/command-r7b-hf).
+
+### **Multilingual RAG Capabilities:**
+
+Cohere Labs Command R7B Arabic has been trained specifically for tasks such as the generation step of Retrieval Augmented Generation (RAG) in Arabic and English. 
+
+RAG with Cohere Labs Command R7B Arabic is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-retrieval-augmented-generation) in Transformers. Using our RAG chat template, the model takes a conversation (with an optional user-supplied system preamble), along with a list of document snippets, as input. The resulting output contains a response with in-line citations. 
+
+<details>
+<summary><b>RAG Example [CLICK TO EXPAND]</b></summary>
+
+```py
+# Define conversation input
+conversation = [{"role": "user", "content": "اقترح طبقًا يمزج نكهات من عدة دول عربية"}]
+
+# Define documents for retrieval-based generation
+documents = [ 
+{"heading": "المطبخ العربي: أطباقنا التقليدية", "body": "يشتهر المطبخ العربي بأطباقه الغنية والنكهات الفريدة. في هذا المقال، سنستكشف ..."},
+    	{"heading": "وصفة اليوم: مقلوبة", "body": "المقلوبة هي طبق فلسطيني تقليدي، يُحضر من الأرز واللحم أو الدجاج والخضروات. في وصفتنا اليوم ..."} 
+]
+
+# Get the RAG prompt
+input_prompt = tokenizer.apply_chat_template(conversation=conversation,documents=documents, tokenize=False, add_generation_prompt=True, return_tensors="pt")
+# Tokenize the prompt
+input_ids = tokenizer.encode_plus(input_prompt, return_tensors="pt")
+```
+
+You can then generate text from this input as usual.
+
+Document snippets should be short chunks, rather than long documents, typically around 100-400 words per chunk, formatted as key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. 
+
+You may find that simply including relevant documents directly in a user message works just as well or better than using the documents parameter to render the special RAG template. The RAG template is generally a strong default and is ideal for users wanting citations. We encourage users to play with both and evaluate which mode works best for their use case.
+</details>
+
+Note that this was a very brief introduction to RAG \- for more information, see the Cohere Labs Command R7B Arabic prompt format docs and the Transformers [RAG documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-retrieval-augmented-generation).
+
+## **Model Card Contact**
+
+For errors or additional questions about details in this model card, contact labs@cohere.com
+
+## **Terms of Use:**
+
+By releasing the weights of a highly performant 7 billion parameter model, we hope to make community-based research efforts more accessible to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/cohere-labs-cc-by-nc-license), requires also adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy)
+
+## **Try Chat:**
+
+You can try Cohere Labs Command R7B Arabic chat in the playground [here](https://dashboard.cohere.com/playground/chat?model=command-r7b-arabic-02-2025). You can also use it in our dedicated Hugging Face Space [here](https://coherelabs-c4ai-command.hf.space/models/command-r7b-arabic-02-2025).
+
+
+## **Citation:**
+
+```
+@misc{alnumay2025command,
+    title={Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM},
+    author={Yazeed Alnumay and Alexandre Barbet and Anna Bialas and William Darling and Shaan Desai and Joan Devassy and Kyle Duffy and Stephanie Howe and Olivia Lasche and Justin Lee and Anirudh Shrinivason and Jennifer Tracey},
+    year={2025},
+    eprint={2503.14603},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "architectures": [
+    "Cohere2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 5,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 255001,
+  "head_dim": 128,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "layer_norm_eps": 1e-05,
+  "logit_scale": 0.25,
+  "max_position_embeddings": 16384,
+  "model_type": "cohere2",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "order_of_interleaved_layers": "local_attn_first",
+  "pad_token_id": 0,
+  "position_embedding_type": "rope_gptj",
+  "rope_scaling": null,
+  "rope_theta": 50000,
+  "rotary_pct": 1.0,
+  "sliding_window": 4096,
+  "sliding_window_pattern": 4,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.0.dev0",
+  "use_cache": true,
+  "use_embedding_sharing": true,
+  "use_gated_activation": true,
+  "use_parallel_block": true,
+  "use_parallel_embedding": true,
+  "vocab_size": 256000
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 5,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 255001,
+  "pad_token_id": 0,
+  "transformers_version": "4.48.0.dev0"
+}

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+    },
+    "255012": {
+      "content": "<|USER_3_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255013": {
+      "content": "<|USER_4_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255014": {
+      "content": "<|USER_5_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255015": {
+      "content": "<|USER_6_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255016": {
+      "content": "<|USER_7_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255017": {
+      "content": "<|USER_8_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255018": {
+      "content": "<|USER_9_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255019": {
+      "content": "<|START_THINKING|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255020": {
+      "content": "<|END_THINKING|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255021": {
+      "content": "<|START_RESPONSE|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "255022": {
+      "content": "<|END_RESPONSE|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "255023": {
+      "content": "<|START_ACTION|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255024": {
+      "content": "<|END_ACTION|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255025": {
+      "content": "<|START_TOOL_RESULT|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255026": {
+      "content": "<|END_TOOL_RESULT|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255027": {
+      "content": "<|EXTRA_8_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255028": {
+      "content": "<|NEW_FILE|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "255029": {
+      "content": "<|BEGINNING_OF_PREFIX_FIM_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255030": {
+      "content": "<|BEGINNING_OF_MIDDLE_FIM_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255031": {
+      "content": "<|BEGINNING_OF_SUFFIX_FIM_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255032": {
+      "content": "<|END_OF_MIDDLE_FIM_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|START_RESPONSE|>",
+    "<|END_RESPONSE|>"
+  ],
+  "bos_token": "<BOS_TOKEN>",
+  "chat_template": [
+    {
+      "name": "default",
+      "template": "{{ bos_token }}{% if documents %}\n{% set tools = [] %}\n{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n    {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n    {\n        \"tool_call_id\": \"0\",\n        \"results\": {\n{% for doc in documents %}\n            \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n            {% endif %}\n{% endfor %}\n\n        },\n        \"is_error\": null\n    }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n    {%- if msg.tool_calls %}\n        {%- for tool_call in msg.tool_calls %}\n            {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n                {{ counter.value }}\n                {%- set tool_call_id_seen.value = true %}\n            {%- endif %}\n            {%- set counter.value = counter.value + 1 %}\n        {%- endfor %}\n    {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n    {\n        \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n        \"results\": {\n            \"0\": {\"text\": \"{{ tool_msg.content }}\"}\n        },\n        \"is_error\": null\n    }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n    NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n    When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n    Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n    NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n    {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n    {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']['properties']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n    {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n    {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n    {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n    {% for tc in message.tool_calls %}\n    {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n    {% set tool_idx.value = tool_idx.value + 1 %}\n    {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n    {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n    {%- for msg in messages[loop.index0 + 1:] %}\n        {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n            {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n        {%- else %}\n            {%- break %}\n        {%- endif %}\n    {%- endfor %}\n    \n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n    {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>\n{%- else -%}\n{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}\n    {%- set system_message = messages[0]['content'] %}{% elif false == true %}\n    {%- set loop_messages = messages %}{% set system_message = '' %}\n{%- else %}\n    {%- set loop_messages = messages %}\n    {%- set system_message = false %}\n{%- endif %}\n{%- if system_message != false -%}\n    {{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}\n{%- else -%}\n    {{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|END_OF_TURN_TOKEN|>' }}\n{%- endif %}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif -%}\n    {%- set content = message['content'] -%}\n    {%- if message['role'] == 'user' -%}\n        {{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}\n    {%- elif message['role'] == 'assistant' -%}\n        {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>'  + content.strip() + '<|END_RESPONSE|><|END_OF_TURN_TOKEN|>' }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt -%}\n    {{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>' }}\n{%- endif %}\n{% endif %}"
+    },
+    {
+      "name": "tool_use",
+      "template": "{{ bos_token }}{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n    {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n    {\n        \"tool_call_id\": \"0\",\n        \"results\": {\n{% for doc in documents %}\n            \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n            {% endif %}\n{% endfor %}\n\n        },\n        \"is_error\": null\n    }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n    {%- if msg.tool_calls %}\n        {%- for tool_call in msg.tool_calls %}\n            {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n                {{ counter.value }}\n                {%- set tool_call_id_seen.value = true %}\n            {%- endif %}\n            {%- set counter.value = counter.value + 1 %}\n        {%- endfor %}\n    {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n    {\n        \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n        \"results\": {\n            \"0\": {\"text\": \"{{ tool_msg.content }}\"}\n        },\n        \"is_error\": null\n    }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n    NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n    When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n    Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n    NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n    {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n    {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']['properties']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n    {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n    {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n    {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n    {% for tc in message.tool_calls %}\n    {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n    {% set tool_idx.value = tool_idx.value + 1 %}\n    {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n    {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n    {%- for msg in messages[loop.index0 + 1:] %}\n        {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n            {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n        {%- else %}\n            {%- break %}\n        {%- endif %}\n    {%- endfor %}\n    \n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n    {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+    },
+    {
+      "name": "rag",
+      "template": "{{ bos_token }}{% set tools = [] %}\n{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n    {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n    {\n        \"tool_call_id\": \"0\",\n        \"results\": {\n{% for doc in documents %}\n            \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n            {% endif %}\n{% endfor %}\n\n        },\n        \"is_error\": null\n    }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n    {%- if msg.tool_calls %}\n        {%- for tool_call in msg.tool_calls %}\n            {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n                {{ counter.value }}\n                {%- set tool_call_id_seen.value = true %}\n            {%- endif %}\n            {%- set counter.value = counter.value + 1 %}\n        {%- endfor %}\n    {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n    {\n        \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n        \"results\": {\n            \"0\": {\"text\": \"{{ tool_msg.content }}\"}\n        },\n        \"is_error\": null\n    }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n    NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n    When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n    Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n    You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n    NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n    {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n    {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']['properties']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n    {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n    {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n    {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n    {% for tc in message.tool_calls %}\n    {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n    {% set tool_idx.value = tool_idx.value + 1 %}\n    {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n    {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n    {%- for msg in messages[loop.index0 + 1:] %}\n        {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n            {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n        {%- else %}\n            {%- break %}\n        {%- endif %}\n    {%- endfor %}\n    \n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n    {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+    }
+  ],
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|END_OF_TURN_TOKEN|>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "merges_file": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<PAD>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "CohereTokenizer",
+  "unk_token": "<UNK>",
+  "use_default_system_prompt": false,
+  "vocab_file": null
+}