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.gitattributes

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

README.md

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+---
+license: llama3.1
+library_name: transformers
+base_model:
+- meta-llama/Llama-3.1-405B
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img src="images/deep-cogito-logo.png" alt="Logo" width="40%">
+</p>
+
+
+# Cogito v2 preview - 405B
+
+[Blog Post](https://www.deepcogito.com/research/cogito-v2-preview)
+
+The Cogito v2 LLMs are instruction tuned generative models. All models are released under an open license for commercial use.
+
+- Cogito v2 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 v2-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
+For detailed evaluations, please refer to the [Blog Post](https://www.deepcogito.com/research/cogito-v2-preview).
+
+
+# Usage
+Here is a snippet below for usage with Transformers:
+
+```python
+import transformers
+import torch
+
+model_id = "deepcogito/cogito-v2-preview-llama-405B"
+
+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:
+  - Set `enable_thinking=True` while applying the chat template.
+  - Add a specific system prompt, along with prefilling the response with "\<think\>\n". 
+
+**NOTE: Unlike Cogito v1 models, we initiate the response with "\<think\>\n" at the beginning of every output when reasoning is enabled. This is because hybrid models can be brittle at times (<0.1% of the cases), and adding a "\<think\>\n" ensures that the model does indeed respect thinking.**
+
+### Method 1 - 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-v2-preview-llama-405B"
+
+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)
+```
+
+### Method 2 - Add a specific system prompt, along with prefilling the response with "\<think\>\n". 
+To enable thinking, you need to do two parts - 
+
+
+Step 1 - 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`.
+
+Step 2 - Prefil the response with the tokens `"<think>\n"`.
+
+Here is an example - 
+
+```python
+import transformers
+import torch
+
+model_name = "deepcogito/cogito-v2-preview-llama-405B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Step 1 - Add deep thinking instruction.
+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."},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Step 2 - Prefill response with "<think>\n".
+text += "<think>\n"
+
+# Now, continue as usual.
+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)
+```
+
+
+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}
+]
+```
+
+
+# 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><|eot_id|>
+```
+
+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.<|eot_id|>'
+```
+
+## License
+This repository and the model weights are licensed under the [Llama 3.3 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE) (Llama models' default license agreement).
+
+## Contact
+If you would like to reach out to our team, send an email to [[email protected]]([email protected]).

chat_template.jinja

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+{{- bos_token }}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+{%- if not enable_thinking is defined %}
+    {%- set enable_thinking = false %}
+{%- endif %}
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+{#- Set the system message. If enable_thinking is true, add the "Enable deep thinking subroutine." #}
+{%- if enable_thinking %}
+    {%- if system_message != "" %}
+        {%- set system_message = "Enable deep thinking subroutine.
+
+" ~ system_message %}
+    {%- else %}
+        {%- set system_message = "Enable deep thinking subroutine." %}
+    {%- endif %}
+{%- endif %}
+{#- Set the system message. In case there are tools present, add them to the system message. #}
+{%- if tools is not none or system_message != '' %}
+    {{- "<|start_header_id|>system<|end_header_id|>
+
+" }}
+    {{- system_message }}
+    {%- if tools is not none %}
+        {%- if system_message != "" %}
+            {{- "
+
+" }}
+        {%- endif %}
+        {{- "Available Tools:
+" }}
+        {%- for t in tools %}
+            {{- t | tojson(indent=4) }}
+            {{- "
+
+" }}
+        {%- endfor %}
+    {%- endif %}
+    {{- "<|eot_id|>" }}
+{%- endif %}
+
+{#- Rest of the messages #}
+{%- for message in messages %}
+    {#- The special cases are when the message is from a tool (via role ipython/tool/tool_results) or when the message is from the assistant, but has "tool_calls". If not, we add the message directly as usual. #}
+    {#- Case 1 - Usual, non tool related message. #}
+    {%- if not (message.role == "ipython" or message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+' }}
+        {%- if message['content'] is string %}
+            {{- message['content'] | trim }}
+        {%- else %}
+            {%- for item in message['content'] %}
+                {%- if item.type == 'text' %}
+                    {{- item.text | trim }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|eot_id|>' }}
+    
+    {#- Case 2 - the response is from the assistant, but has a tool call returned. The assistant may also have returned some content along with the tool call. #}
+    {%- elif message.tool_calls is defined and message.tool_calls is not none %}
+        {{- "<|start_header_id|>assistant<|end_header_id|>
+
+" }}
+        {%- if message['content'] is string %}
+            {{- message['content'] | trim }}
+        {%- else %}
+            {%- for item in message['content'] %}
+                {%- if item.type == 'text' %}
+                    {{- item.text | trim }}
+                    {%- if item.text | trim != "" %}
+                        {{- "
+
+" }}
+                    {%- endif %}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- "[" }}
+        {%- for tool_call in message.tool_calls %}
+            {%- set out = tool_call.function|tojson %}
+            {%- if not tool_call.id is defined %}
+                {{- out }}
+            {%- else %}
+                {{- out[:-1] }}
+                {{- ', "id": "' + tool_call.id + '"}' }}
+            {%- endif %}
+            {%- if not loop.last %}
+                {{- ", " }}
+            {%- else %}
+                {{- "]<|eot_id|>" }}
+            {%- endif %}
+        {%- endfor %}
+    
+    {#- Case 3 - the response is from a tool call. The tool call may have an id associated with it as well. If it does, we add it to the prompt. #}
+    {%- elif message.role == "ipython" or message["role"] == "tool_results" or message["role"] == "tool" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>
+
+" }}
+        {%- if message.tool_call_id is defined and message.tool_call_id != '' %}
+            {{- '{"content": ' + (message.content | tojson) + ', "call_id": "' + message.tool_call_id + '"}' }}
+        {%- else %}
+            {{- '{"content": ' + (message.content | tojson) + '}' }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' }}
+    {%- if enable_thinking %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 16384,
+  "initializer_range": 0.02,
+  "intermediate_size": 53248,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 128,
+  "num_hidden_layers": 126,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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special_tokens_map.json

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