Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,241 +1,255 @@
----
-license: apache-2.0
-library_name: transformers
-base_model:
-- Qwen/Qwen2.5-14B
-pipeline_tag: text-generation
----
-
-<p align="center">
-  <img src="images/deep-cogito-logo.png" alt="Logo" width="40%">
-</p>
-
-
-# Cogito v1 preview - 14B
-
-[Blog Post](https://www.deepcogito.com/research/cogito-v1-preview)
-
-The Cogito LLMs are instruction tuned generative models (text in/text out). All models are released under an open license for commercial use.
-
-- Cogito models are hybrid reasoning models. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models).
-- The LLMs are trained using **Iterated Distillation and Amplification (IDA)** - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.
-- The models have been optimized for coding, STEM, instruction following and general helpfulness, and have significantly higher multilingual, coding and tool calling capabilities than size equivalent counterparts.
-  - In both standard and reasoning modes, Cogito v1-preview models outperform their size equivalent counterparts on common industry benchmarks. 
-- Each model is trained in over 30 languages and supports a context length of 128k.
-
-# Evaluations
-We compare our models against the state of the art size equivalent models in direct mode as well as the reasoning mode. For the direct mode, we compare against Llama / Qwen instruct counterparts. For reasoning, we use Deepseek's R1 distilled counterparts / Qwen's QwQ model.
-
-<p align="left">
-  <img src="images/14b_benchmarks.png" alt="Logo" width="90%">
-</p>
-
-**Livebench Global Average:**
-<p align="left">
-  <img src="images/livebench_global_average.png" alt="Logo" width="80%">
-</p>
-
-For detailed evaluations, please refer to the [Blog Post](https://www.deepcogito.com/research/cogito-v1-preview).
-
-
-# Usage
-Here is a snippet below for usage with Transformers:
-
-```python
-import transformers
-import torch
-
-model_id = "deepcogito/cogito-v1-preview-qwen-14B"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
-
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Give me a short introduction to LLMs."},
-]
-
-outputs = pipeline(
-    messages,
-    max_new_tokens=512,
-)
-
-print(outputs[0]["generated_text"][-1])
-```
-
-
-
-## Implementing extended thinking
-- By default, the model will answer in the standard mode. 
-- To enable thinking, you can do any one of the two methods:
-  - Add a specific system prompt, or 
-  - Set `enable_thinking=True` while applying the chat template.
-
-
-### Method 1 - Add a specific system prompt.
-To enable thinking, simply use this in the system prompt `system_instruction = 'Enable deep thinking subroutine.'`
-
-If you already have a system_instruction, then use `system_instruction = 'Enable deep thinking subroutine.' + '\n\n' + system_instruction`.
-
-Here is an example - 
-
-```python
-import transformers
-import torch
-
-model_id = "deepcogito/cogito-v1-preview-qwen-14B"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
-
-DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
-
-messages = [
-    {"role": "system", "content": DEEP_THINKING_INSTRUCTION},
-    {"role": "user", "content": "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."},
-]
-
-outputs = pipeline(
-    messages,
-    max_new_tokens=512,
-)
-
-print(outputs[0]["generated_text"][-1])
-```
-
-
-Similarly, if you have a system prompt, you can append the `DEEP_THINKING_INSTRUCTION` to the beginning in this way - 
-
-```python
-DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
-
-system_prompt = "Reply to each prompt with only the actual code - no explanations."
-prompt = "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."
-
-messages = [
-    {"role": "system", "content": DEEP_THINKING_INSTRUCTION + '\n\n' + system_prompt},
-    {"role": "user", "content": prompt}
-]
-```
-
-### Method 2 - Set enable_thinking=True in the tokenizer
-If you are using Huggingface tokenizers, then you can simply use add the argument `enable_thinking=True` to the tokenization (this option is added to the chat template).
-
-Here is an example - 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "deepcogito/cogito-v1-preview-qwen-14B"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to LLMs."
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": prompt}
-]
-
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-    enable_thinking=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(response)
-```
-
-# Tool Calling
-Cogito models support tool calling (single, parallel, multiple and parallel_multiple) both in standard and extended thinking mode.
-
-Here is a snippet -
-
-```python
-# First, define a tool
-def get_current_temperature(location: str) -> float:
-    """
-    Get the current temperature at a location.
-    
-    Args:
-        location: The location to get the temperature for, in the format "City, Country"
-    Returns:
-        The current temperature at the specified location in the specified units, as a float.
-    """
-    return 22.  # A real function should probably actually get the temperature!
-
-# Next, create a chat and apply the chat template
-messages = [
-  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
-]
-
-model_inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
-
-text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
-inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=512)
-output_text = tokenizer.batch_decode(outputs)[0][len(text):]
-print(output_text)
-```
-
-This will result in the output - 
-```
-<tool_call>
-{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
-</tool_call><|im_end|>
-```
-
-You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
-
-```python
-tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
-messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
-```
-
-and then call the tool and append the result, with the `tool` role, like so:
-
-```python
-messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
-```
-
-After that, you can `generate()` again to let the model use the tool result in the chat:
-
-```python
-text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
-inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=512)
-output_text = tokenizer.batch_decode(outputs)[0][len(text):]
-```
-
-This should result in the string -
-```
-'The current temperature in Paris is 22.0 degrees.<|im_end|>'
-```
-
-## License
-This repository and the model weights are licensed under the Apache 2.0 License Agreement.
-
-## Contact
-If you would like to reach out to our team, send an email to [[email protected]](contact@deepcogito.com).
+---
+license: apache-2.0
+library_name: transformers
+base_model:
+- Qwen/Qwen2.5-14B
+pipeline_tag: text-generation
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+<p align="center">
+  <img src="images/deep-cogito-logo.png" alt="Logo" width="40%">
+</p>
+
+
+# Cogito v1 preview - 14B
+
+[Blog Post](https://www.deepcogito.com/research/cogito-v1-preview)
+
+The Cogito LLMs are instruction tuned generative models (text in/text out). All models are released under an open license for commercial use.
+
+- Cogito models are hybrid reasoning models. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models).
+- The LLMs are trained using **Iterated Distillation and Amplification (IDA)** - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.
+- The models have been optimized for coding, STEM, instruction following and general helpfulness, and have significantly higher multilingual, coding and tool calling capabilities than size equivalent counterparts.
+  - In both standard and reasoning modes, Cogito v1-preview models outperform their size equivalent counterparts on common industry benchmarks. 
+- Each model is trained in over 30 languages and supports a context length of 128k.
+
+# Evaluations
+We compare our models against the state of the art size equivalent models in direct mode as well as the reasoning mode. For the direct mode, we compare against Llama / Qwen instruct counterparts. For reasoning, we use Deepseek's R1 distilled counterparts / Qwen's QwQ model.
+
+<p align="left">
+  <img src="images/14b_benchmarks.png" alt="Logo" width="90%">
+</p>
+
+**Livebench Global Average:**
+<p align="left">
+  <img src="images/livebench_global_average.png" alt="Logo" width="80%">
+</p>
+
+For detailed evaluations, please refer to the [Blog Post](https://www.deepcogito.com/research/cogito-v1-preview).
+
+
+# Usage
+Here is a snippet below for usage with Transformers:
+
+```python
+import transformers
+import torch
+
+model_id = "deepcogito/cogito-v1-preview-qwen-14B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Give me a short introduction to LLMs."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+
+print(outputs[0]["generated_text"][-1])
+```
+
+
+
+## Implementing extended thinking
+- By default, the model will answer in the standard mode. 
+- To enable thinking, you can do any one of the two methods:
+  - Add a specific system prompt, or 
+  - Set `enable_thinking=True` while applying the chat template.
+
+
+### Method 1 - Add a specific system prompt.
+To enable thinking, simply use this in the system prompt `system_instruction = 'Enable deep thinking subroutine.'`
+
+If you already have a system_instruction, then use `system_instruction = 'Enable deep thinking subroutine.' + '\n\n' + system_instruction`.
+
+Here is an example - 
+
+```python
+import transformers
+import torch
+
+model_id = "deepcogito/cogito-v1-preview-qwen-14B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
+
+messages = [
+    {"role": "system", "content": DEEP_THINKING_INSTRUCTION},
+    {"role": "user", "content": "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+
+print(outputs[0]["generated_text"][-1])
+```
+
+
+Similarly, if you have a system prompt, you can append the `DEEP_THINKING_INSTRUCTION` to the beginning in this way - 
+
+```python
+DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
+
+system_prompt = "Reply to each prompt with only the actual code - no explanations."
+prompt = "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."
+
+messages = [
+    {"role": "system", "content": DEEP_THINKING_INSTRUCTION + '\n\n' + system_prompt},
+    {"role": "user", "content": prompt}
+]
+```
+
+### Method 2 - Set enable_thinking=True in the tokenizer
+If you are using Huggingface tokenizers, then you can simply use add the argument `enable_thinking=True` to the tokenization (this option is added to the chat template).
+
+Here is an example - 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "deepcogito/cogito-v1-preview-qwen-14B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to LLMs."
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+# Tool Calling
+Cogito models support tool calling (single, parallel, multiple and parallel_multiple) both in standard and extended thinking mode.
+
+Here is a snippet -
+
+```python
+# First, define a tool
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+    
+    Args:
+        location: The location to get the temperature for, in the format "City, Country"
+    Returns:
+        The current temperature at the specified location in the specified units, as a float.
+    """
+    return 22.  # A real function should probably actually get the temperature!
+
+# Next, create a chat and apply the chat template
+messages = [
+  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
+]
+
+model_inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
+
+text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+output_text = tokenizer.batch_decode(outputs)[0][len(text):]
+print(output_text)
+```
+
+This will result in the output - 
+```
+<tool_call>
+{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+</tool_call><|im_end|>
+```
+
+You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
+
+```python
+tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
+```
+
+and then call the tool and append the result, with the `tool` role, like so:
+
+```python
+messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
+```
+
+After that, you can `generate()` again to let the model use the tool result in the chat:
+
+```python
+text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+output_text = tokenizer.batch_decode(outputs)[0][len(text):]
+```
+
+This should result in the string -
+```
+'The current temperature in Paris is 22.0 degrees.<|im_end|>'
+```
+
+## License
+This repository and the model weights are licensed under the Apache 2.0 License Agreement.
+
+## Contact
+If you would like to reach out to our team, send an email to [[email protected]]([email protected]).