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.gitattributes

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

README.md

@@ -0,0 +1,289 @@
+---
+inference: false
+library_name: transformers
+language:
+- en
+- ar
+- bg
+- bn
+- ca
+- cs
+- da
+- de
+- el
+- es
+- et
+- fa
+- fi
+- fil
+- fr
+- ga
+- he
+- hi
+- hr
+- hu
+- id
+- is
+- it
+- ja
+- ko
+- lt
+- lv
+- ms
+- mt
+- nl
+- 'no'
+- pa
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sr
+- sv
+- ta
+- te
+- th
+- tr
+- uk
+- ur
+- vi
+- zh
+license: apache-2.0
+base_model: CohereLabs/command-a-plus-05-2026
+base_model_relation: quantized
+pipeline_tag: image-text-to-text
+tags:
+- conversational
+- chat
+---
+
+# **Model Card for Command A+**
+
+## **Model Summary**
+
+Command A+ is an open source model with 25 billion active parameters and 218B total parameters model optimized for agentic, multilingual, and reasoning-heavy tasks with a focus on enterprise performance, while also providing support for vision inputs for processing image inputs.
+
+Developed by: [Cohere](https://cohere.com/) and [Cohere Labs](https://cohere.com/research)
+
+* Point of Contact: [**Cohere Labs**](https://cohere.com/research)
+* License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+* Model: command-a-plus-05-2026
+* Model Size: 25B active parameters, 218B total parameters
+* Context length: 128K input
+
+For more details about this model, please check out our [blog post](http://cohere.com/blog/command-a-plus).
+
+You can try out Command A+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/command-a-plus-05-2026).
+
+**Available quantizations**
+
+The following quantizations are available with example minimum GPU requirements
+
+| Quantization | Blackwell | Hopper |
+| :---- | :---- | :---- |
+| [BF16 (16-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-bf16) | 4 x B200 | 8 x H100 |
+| [FP8 (8-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-fp8) | 2 x B200 | 4 x H100 |
+| [W4A4 (4-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-w4a4) | 1 x B200 | 2 x H100 |
+
+All three quantizations show negligible differences in benchmark quality and performance. **Our recommended quantization for most uses is [W4A4](https://huggingface.co/CohereLabs/command-a-plus-05-2026-w4a4) which boasts superior speed and latency characteristics alongside a smaller hardware footprint.**
+
+For more details, please check out our [blog post](http://cohere.com/blog/command-a-plus).
+
+**Usage**
+
+**Transformers**
+
+Please install transformers from the source repository that includes the necessary changes for this model.
+
+```py
+# pip install transformers
+from transformers import AutoTokenizer, AutoModelForImageTextToText
+
+model_id = "CohereLabs/command-a-plus-05-2026-fp8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id)
+
+# Format message with the command-a-plus-05-2026-fp8 chat template
+messages = [{"role": "user", "content": "What has keys but can't open locks?"}]
+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=4096,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95
+)
+
+gen_text = tokenizer.decode(gen_tokens[0])
+print(gen_text)
+```
+
+As a result, you should get an output that looks like this, where the thinking is generated between the `<START_THINKING>` and `<END_THINKING>`:
+
+```py
+<|START_THINKING|>The user asks a riddle: "What has keys but can't open locks?" The answer is a piano (or keyboard). So respond with answer.<|END_THINKING|>
+```
+
+You can also use the model directly using transformers pipeline abstraction:
+
+```py
+from transformers import pipeline
+import torch
+
+model_id = "CohereLabs/command-a-plus-05-2026-fp8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Explain the Transformer architecture"},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+outputs = pipe(
+    messages,
+    max_new_tokens=300,
+)
+print(outputs[0]["generated_text"][-1])
+
+
+```
+
+**vLLM**
+
+You can also run the model in vLLM. `vllm>=0.21.0` is required for Command A+ and accurate response parsing also requires installing [Cohere’s `melody` library](https://pypi.org/project/cohere-melody/).
+
+```
+uv pip install vllm>=0.21.0
+uv pip install transformers uv pip install cohere_melody>=0.9.0
+```
+
+Then the vllm server can be started with the following command:
+
+```
+# This is for B200, adjust tp for your device vllm serve CohereLabs/command-a-plus-05-2026-fp8 -tp 4 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice
+```
+
+## **Model Details**
+
+**Input**: Text and images.
+
+**Output**: Model generates text.
+
+**Model Architecture**: Command A+ is a decoder-only Sparse Mixture-of-Experts Transformer Model. With 25B active parameters and 218B total parameters, it has 128 experts, out of which 8 are active per token, and a single shared expert is applied to all tokens. The attention layers interleave sliding-window attention layers with Rotational Positional Embeddings and global attention layers without positional embeddings in a 3:1 ratio, as first introduced in Command A. The sparse MoE layer is trained in a fully dropless manner and uses a token-choice router. We use additive-bias-based load balancing to encourage balanced token load across all experts, and swap out the softmax router activation function with a normalized sigmoid over the topk expert logits per token.
+
+**Languages covered:** The model has been trained on 48 languages: English, Arabic, Bulgarian, Bengali, Catalan, Czech, Danish, German, Greek, Spanish, Estonian, Persian, Finnish, Filipino, French, Irish, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Icelandic, Italian, Japanese, Korean, Lithuanian, Latvian, Malay, Maltese, Dutch, Norwegian, Punjabi, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Serbian, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Chinese.
+
+**Context Length:** Command A+ supports a context length of 128K & 64K output length.
+
+### **Tool Use Capabilities:**
+
+Command A+ has been specifically trained with conversational tool use capabilities. This allows the model to interact with external tools like APIs, databases, or search engines.
+
+Tool use with Command A+ is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema.
+
+<details>
+<summary><b>Tool Use Example [CLICK TO EXPAND]</b></summary>
+
+```py
+from transformers import AutoTokenizer
+
+model_id = "CohereLabs/command-a-plus-05-2026-fp8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Define tools
+tools = [{
+    "type": "function",
+    "function": {
+        "name": "query_daily_sales_report",
+        "description": "Connects to a database to retrieve overall sales volumes and sales information for a given day.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "day": {
+                    "description": "Retrieves sales data for this day, formatted as YYYY-MM-DD.",
+                    "type": "string",
+                }
+            },
+            "required": ["day"],
+        },
+    },
+}]
+
+# Define conversation input
+conversation = [
+    {"role": "user", "content": "Can you provide a sales summary for 29th September 2023?"}
+]
+
+# Tokenize the Tool Use prompt directly
+input_ids = tokenizer.apply_chat_template(
+    conversation=conversation,
+    tools=tools,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+)
+```
+
+You can then generate from this input as normal.
+
+If the model generates a plan and tool calls, you should add them to the chat history like so:
+
+```py
+tool_call = {"name": "query_daily_sales_report", "arguments": {"day": "2023-09-29"}}
+thinking = "I will use the query_daily_sales_report tool to find the sales summary for 29th September 2023."
+conversation.append({"role": "assistant", "tool_calls": [{"id": "0", "type": "function", "function": tool_call}], "thinking": thinking})
+```
+
+and then call the tool and append the result, as a dictionary, with the tool role, like so:
+
+```py
+api_response_query_daily_sales_report = {"date": "2023-09-29", "summary": "Total Sales Amount: 10000, Total Units Sold: 250"} # this needs to be a dictionary!!
+
+# Append tool results
+conversation.append({"role": "tool", "tool_call_id": "0", "content": api_response_query_daily_sales_report})
+```
+
+After that, you can generate() again to let the model use the tool result in the chat.
+
+Note that this was a very brief introduction to tool calling \- for more information, see the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
+</details>
+
+<details>
+<summary><b>Tool Use With Citations [CLICK TO EXPAND]</b></summary>
+
+Optionally, one can ask the model to include grounding spans (citations) in its response to indicate the source of the information, by using `enable_citations=True` in `tokenizer.apply_chat_template(*)`. The generation would look like this:
+
+```
+On 29th September 2023, the total sales amount was <co>10000</co: 0:[0]> and the total units sold were <co>250.</co: 0:[0]>
+```
+
+When citations are turned on, the model associates pieces of texts (called "spans") with those specific tool results that support them (called "sources"). Command A+ uses 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. For example, `<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`. 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 ",".
+</details>
+
+
+## **Model Card Contact**
+
+For errors or additional questions about details in this model card, contact \[[labs@cohere.com](mailto:labs@cohere.com)\].
+
+**Try it now:**
+
+You can try Command A+ in the [playground](https://dashboard.cohere.com/playground/chat?model=command-a-plus-05-2026). You can also use it in our dedicated [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/command-a-plus-05-2026).

chat_template.jinja

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+{%- set reasoning = reasoning if reasoning is not undefined else (false if reasoning_effort is defined and reasoning_effort | lower == "none" else true) -%}
+{%- set grounding = grounding | default("disabled") | upper %}
+{%- set grounding_enabled = grounding == "ENABLED" %}
+{%- set tools_or_docs_exist = tools or documents %}
+{%- set render_tools_section = true %}
+{%- set render_grounding = grounding_enabled and tools_or_docs_exist %}
+{%- set render_platform_instruction_override = true if platform_instruction_override else false %}
+{%- set has_developer_instruction = developer_instruction or developer_instruction == "" %}
+{%- set render_developer_instruction = true if developer_instruction else false %}
+{%- set convert_first_system_msg = convert_first_system_msg | default(true) -%}
+{%- set skip_thinking = skip_thinking | default(false) -%}
+{{ bos_token }}
+{%- macro document_turn(documents) -%}
+{# format documents into chat turn -#}
+<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if not skip_thinking -%}<|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|>{%- endif -%}<|START_ACTION|>[
+    {"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}}
+]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
+    {
+        "tool_call_id": "0",
+        "results": {
+{%- for doc in documents %}
+{%- set doc_val = doc.data if doc.data else doc %}
+
+            "{{ loop.index0 }}": {{ doc_val|tojson }}{% if not loop.last %},
+            {%- endif %}
+{%- endfor %}
+
+        },
+        "is_error": null
+    }
+]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}
+{%- macro tool_call_id_to_int(messages, tool_call_id) %}
+{%- if regen_tool_call_ids -%}
+    {%- set counter = namespace(value=0) %}
+    {%- set tool_call_id_seen = namespace(value=false) %}
+    {%- for msg in messages %}
+        {%- if msg.tool_calls %}
+            {%- for tool_call in msg.tool_calls %}
+                {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}
+                    {{ counter.value }}
+                    {%- set tool_call_id_seen.value = true %}
+                {%- endif %}
+                {%- set counter.value = counter.value + 1 %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endfor %}
+{%- else -%}
+    {{ tool_call_id }}
+{%- endif -%}
+{%- endmacro %}
+{%- macro format_tool_message(messages, tool_msg) -%}
+{#- format tool message #}{
+        "tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}",
+        "results": {
+        {%- if tool_msg.content is mapping or tool_msg.content is string %}
+
+            {% if tool_msg.content is string -%}
+                {%- set text_wrapper = {"content": tool_msg.content} -%}
+            {%- else -%}
+                {%- set text_wrapper = tool_msg.content -%}
+            {%- endif %}
+            "0": {{ text_wrapper|tojson }}
+        {%- else %}
+            {%- for content in tool_msg.content %}
+
+            "{{ loop.index0 }}": {{ print_tool_content(content) }}{% if not loop.last %},{% endif %}
+            {%- endfor %}
+        {%- endif %}
+
+        },
+        "is_error": null
+    }
+{%- endmacro -%}
+{%- macro print_tool_content(item) %}
+{%- if item.type|lower == "text" -%}
+{%- set text_wrapper = {"content": item.text} -%}
+{{ text_wrapper|tojson }}
+{%- elif item.type|lower == "document" and item.document and "data" in item.document -%}
+{{ item.document.data|tojson }}
+{%- else -%}
+{{ item|tojson }}
+{%- endif -%}
+{%- endmacro %}
+{%- macro print_msg(msg) %}
+    {%- if msg is string -%}
+<|START_TEXT|>{{ msg }}<|END_TEXT|>
+    {%- elif msg.content is string -%}
+<|START_TEXT|>{{ msg.content }}<|END_TEXT|>
+    {%- else %}
+        {%- set last_was_text = namespace(value=false) %}
+        {%- for content in msg.content %}
+            {%- if content.type|lower == "text" -%}
+                {%- if not last_was_text.value -%}
+                    <|START_TEXT|>
+                {%- endif -%}
+    {{ content.text }}
+                {%- if loop.last -%}
+                  <|END_TEXT|>
+                {%- endif %}
+                {%- set last_was_text.value = true -%}
+            {%- else -%}
+                {%- if last_was_text.value -%}
+                    <|END_TEXT|>
+                {%- endif -%}
+                {%- set last_was_text.value = false -%}
+            {%- endif -%}
+            {%- if content.type|lower == "image" -%}
+                {%- if content.data -%}
+{{ content.data }}
+                {%- else -%}
+<|IMG_PATCH|>
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor %}
+    {%- endif %}
+{%- endmacro %}
+{%- macro print_thinking(msg) %}
+    {%- if msg.thinking -%}
+{{ msg.thinking }}
+    {%- elif msg.content and msg.content[0].thinking -%}
+{{ msg.content[0].thinking }}
+    {%- endif %}
+{%- endmacro %}
+{%- if messages and messages[0]['role']|lower == 'system' and not has_developer_instruction and convert_first_system_msg %}{%- set developer_instruction = messages[0] %}{%- set render_developer_instruction = true %}{%- set initial_instruction_message = true %}{% endif %}
+{%- set json_object = true if response_format and response_format.type == "json_object" else false %}
+{%- set json_schema = (response_format.json_schema or response_format.schema) if response_format %}
+{%- set json_mode = json_object or json_schema %}
+{%- set tool_idx = namespace(value=0) %}
+{%- set tool_ids_seen = namespace(value=[]) %}
+{%- set regen_tool_call_ids = regen_tool_call_ids | default(true) -%}
+{%- set sent_documents = namespace(value=false) -%}
+
+{%- if render_tools_section or render_platform_instruction_override or render_grounding or json_mode -%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TEXT|>
+{%- elif not render_developer_instruction -%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>
+{%- endif %}
+
+{%- set rendered_platform_turn_chunk = false %}
+
+{%- if render_platform_instruction_override -%}
+{{ platform_instruction_override }}
+{% set rendered_platform_turn_chunk = true %}
+{%- else %}
+{%- endif %}
+
+{%- if render_grounding -%}
+{%- if rendered_platform_turn_chunk %}
+
+{% endif -%}
+Note that both your responses and reflections can be grounded. Grounding 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".
+{% set rendered_platform_turn_chunk = true %}
+{%- endif %}
+
+{%- if render_tools_section %}
+{%- if rendered_platform_turn_chunk %}
+
+{% endif %}
+# Available Tools
+```json
+[
+{% if tools_or_docs_exist %}
+{%- if documents %}
+    {"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 %},
+    {% else %}
+
+    {% endif %}
+{%- endif %}
+{%- for tool in tools %}
+    {"name": "{{ tool['function']['name'] }}", "description": "{{ tool['function']['description'] }}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null}
+    {%- if not loop.last %},{% endif %}
+
+{% endfor %}
+{%- else %}
+
+{% endif %}
+]
+```
+{%- set rendered_platform_turn_chunk = true %}
+{%- endif -%}
+
+{%- if json_mode -%}
+{%- if rendered_platform_turn_chunk %}
+
+
+{% endif -%}
+When generating JSON objects, do not generate block markers. Generate an object directly without prefixing with ```json. Return only the JSON and nothing else.
+    {%- if json_schema %}
+
+Your output should adhere to the following json schema:
+{{ json_schema }}
+    {%- endif -%}
+{%- set rendered_platform_turn_chunk = true %}
+{%- endif %}
+{%- if rendered_platform_turn_chunk -%}
+<|END_TEXT|><|END_OF_TURN_TOKEN|>
+{%- elif not render_developer_instruction -%}
+<|END_OF_TURN_TOKEN|>
+{%- endif %}
+{%- if render_developer_instruction -%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(developer_instruction) }}<|END_OF_TURN_TOKEN|>
+{%- endif %}
+{%- for message in messages %}
+    {%- set msg_role_downcased = message.role | lower %}
+    {%- if msg_role_downcased == 'system' and (not (loop.first and initial_instruction_message)) -%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(message) }}<|END_OF_TURN_TOKEN|>
+    {%- elif msg_role_downcased == 'user' -%}
+<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ print_msg(message) }}<|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 %}
+    {%- elif msg_role_downcased == 'assistant' or msg_role_downcased == 'chatbot' -%}
+<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+        {%- if message.tool_calls %}
+            {% if not skip_thinking %}
+                {% if message.tool_plan -%}
+                    <|START_THINKING|>{{ message.tool_plan }}<|END_THINKING|>
+                {%- elif message.thinking or (message.content and message.content[0].type == "thinking") -%}
+                    <|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|>
+                {%- endif %}
+            {%- endif %}<|START_ACTION|>[
+            {%- for tc in message.tool_calls %}
+
+    {"tool_call_id": "{%- if regen_tool_call_ids -%}{{ tool_idx.value }}{%- else -%}{{ tc.id }}{%- endif -%}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}
+                {%- set tool_idx.value = tool_idx.value + 1 %}
+            {%- endfor %}
+
+]<|END_ACTION|><|END_OF_TURN_TOKEN|>
+        {%- else -%}
+            {% if (message.thinking or (message.content and message.content[0].type == "thinking")) and not skip_thinking -%}
+                <|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|>
+            {%- endif -%}
+            {{ print_msg(message) }}<|END_OF_TURN_TOKEN|>
+        {%- endif %}
+    {%- elif msg_role_downcased == 'tool' and message.tool_call_id not in tool_ids_seen.value -%}
+<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
+    {{ format_tool_message(messages, message) }}
+        {%- for msg in messages[loop.index0 + 1:] %}
+
+            {%- if msg.role | lower == 'tool' %},
+    {{ format_tool_message(messages, msg) }}
+                {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}
+            {%- else %}
+                {%- break %}
+            {%- endif %}
+        {%- endfor %}
+
+]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>
+    {%- endif %}
+{%- endfor %}{%- if add_generation_prompt -%}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if reasoning %}<|START_THINKING|>{% else %}<|START_THINKING|><|END_THINKING|>{% endif %}{%- endif %}

config.json

@@ -0,0 +1,485 @@
+{
+  "adapter_layer_norm_eps": 1e-06,
+  "alignment_activation_fn": "swiglu",
+  "alignment_intermediate_size": 12288,
+  "architectures": [
+    "Cohere2VisionForConditionalGeneration"
+  ],
+  "downsample_factor": 2,
+  "dtype": "bfloat16",
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+  "eoi_token": "<|END_OF_IMG|>",
+  "eos_token": "<|END_OF_TURN_TOKEN|>",
+  "extra_special_tokens": {
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+    "eoi_token": "<|END_OF_IMG|>",
+    "image_token": "<|IMG_PATCH|>",
+    "img_line_break_token": "<|IMG_LINE_BREAK|>"
+  },
+  "image_token": "<|IMG_PATCH|>",
+  "img_line_break_token": "<|IMG_LINE_BREAK|>",
+  "legacy": true,
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+  "pad_token": "<PAD>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<UNK>",
+  "use_default_system_prompt": false,
+  "vocab_file": null
+}