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README.md

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+---
+license: mit
+library_name: transformers
+base_model:
+- deepseek-ai/DeepSeek-V3-Base
+---
+
+<p align="center">
+  <img src="images/deep-cogito-logo.png" alt="Logo" width="40%">
+</p>
+
+
+# Cogito v2 preview - 671B MoE
+
+[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. 
+- This 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-671B-MoE"
+
+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, 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-671B-MoE"
+
+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 using this method, 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-671B-MoE"
+
+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▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_current_temperature
+```json
+{"location":"Paris, France"}
+```<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜>
+```
+
+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.<｜end▁of▁sentence｜>'
+```
+
+## License
+This repository and the model weights are licensed under **MIT License**.
+
+## 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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+{# ==================================================================== #}
+{#  Deepseek v3 template with enable_thinking and tools support          #}
+{# ==================================================================== #}
+{%- if not enable_thinking is defined %}{% set enable_thinking = false %}{% endif -%}
+{%- if not tools is defined %}{% set tools = none %}{% endif -%}
+{%- if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif -%}
+
+{# --------------------------- Collect system prompt -------------------- #}
+{%- set ns = namespace(system_prompt='', is_last_user=false, outputs_open=false, first_output=true) -%}
+
+{%- if messages and messages[0].role == 'system' -%}
+  {%- set raw = messages[0].content -%}
+  {%- set ns.system_prompt = raw if raw is string else raw[0].text -%}
+  {%- set messages = messages[1:] -%}
+{%- endif -%}
+
+{# --------------------------- Inject deep thinking --------------------- #}
+{%- if enable_thinking -%}
+  {%- set ns.system_prompt = ns.system_prompt and 'Enable deep thinking subroutine.
+
+' ~ ns.system_prompt or 'Enable deep thinking subroutine.' -%}
+{%- endif -%}
+
+{# --------------------------- Append tools block ----------------------- #}
+{%- if tools is not none -%}
+  {%- if ns.system_prompt -%}
+    {%- set ns.system_prompt = ns.system_prompt ~ '
+
+You have the following functions available:
+
+' -%}
+  {%- else -%}
+    {%- set ns.system_prompt = 'You have the following functions available:
+
+' -%}
+  {%- endif -%}
+  {%- for t in tools -%}
+    {%- set ns.system_prompt = ns.system_prompt ~ "```json
+" ~ (t | tojson(indent=4)) ~ "
+```
+
+" -%}
+  {%- endfor -%}
+{%- endif -%}
+
+{{- bos_token -}}{{- ns.system_prompt -}}
+
+{# --------------------------- Iterate conversation --------------------- #}
+{%- for m in messages -%}
+  {# --------------------------- USER ---------------------------------- #}
+  {%- if m.role == 'user' -%}
+    {%- set ns.is_last_user = true -%}
+    {%- set txt = m.content if m.content is string else m.content | selectattr('type','equalto','text') | map(attribute='text') | join('') -%}
+    {{- "<｜User｜>" -}}{{- txt -}}{{- "<｜Assistant｜>" -}}
+  {%- endif -%}
+
+  {# --------------------------- ASSISTANT with TOOL CALLS -------------- #}
+  {%- if m.role == 'assistant' and m.tool_calls is defined and m.tool_calls -%}
+    {%- set ns.is_last_user = false -%}
+    {%- set lead = m.content is string and m.content|trim or (m.content and m.content | selectattr('type','equalto','text') | map(attribute='text') | join('')) or '' -%}
+    {{- lead -}}{{- "<｜tool▁calls▁begin｜>" -}}
+    {%- for call in m.tool_calls -%}
+      {{- "<｜tool▁call▁begin｜>" -}}{{- call.type -}}{{- "<｜tool▁sep｜>" -}}{{- call.function.name -}}
+      {{- "
+```json
+" -}}{{- call.function.arguments -}}{{- "
+```" -}}{{- "<｜tool▁call▁end｜>" -}}
+      {%- if not loop.last -%}{{- "
+" -}}{%- endif -%}
+    {%- endfor -%}
+    {{- "<｜tool▁calls▁end｜>" -}}{{- "<｜end▁of▁sentence｜>" -}}
+  {%- endif -%}
+
+  {# --------------------------- ASSISTANT plain ------------------------ #}
+  {%- if m.role == 'assistant' and (m.tool_calls is not defined or not m.tool_calls) -%}
+    {%- set ns.is_last_user = false -%}
+    {%- set txt = m.content if m.content is string else m.content | selectattr('type','equalto','text') | map(attribute='text') | join('') -%}
+    {{- txt -}}{{- "<｜end▁of▁sentence｜>" -}}
+  {%- endif -%}
+
+  {# --------------------------- TOOL output ---------------------------- #}
+  {%- if m.role == 'tool' -%}
+    {%- set ns.is_last_user = false -%}
+    {%- set out_txt = m.content if m.content is string else m.content | selectattr('type','equalto','text') | map(attribute='text') | join('') -%}
+    {%- if not ns.outputs_open -%}
+      {{- "<｜tool▁outputs▁begin｜>" -}}
+      {%- set ns.outputs_open = true -%}
+    {%- endif -%}
+    {{- "<｜tool▁output▁begin｜>" -}}{{- out_txt -}}{{- "<｜tool▁output▁end｜>" -}}
+    {%- if loop.nextitem is defined and loop.nextitem.role == 'tool' -%}
+      {{- "
+" -}}
+    {%- endif -%}
+    {%- if loop.nextitem is undefined or loop.nextitem.role != 'tool' -%}
+      {{- "<｜tool▁outputs▁end｜>" -}}
+      {%- set ns.outputs_open = false -%}
+    {%- endif -%}
+  {%- endif -%}
+{%- endfor -%}
+
+{%- if ns.outputs_open -%}
+  {{- "<｜tool▁outputs▁end｜>" -}}
+{%- endif -%}
+
+{%- if add_generation_prompt and not ns.is_last_user -%}
+  {{- "<｜Assistant｜>" -}}
+{%- endif -%}
+
+{%- if add_generation_prompt and enable_thinking -%}
+  {{- '<think>\n' -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "DeepseekV3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_deepseek.DeepseekV3Config",
+    "AutoModel": "modeling_deepseek.DeepseekV3Model",
+    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
+  },
+  "aux_loss_alpha": 0.001,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 3,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 18432,
+  "kv_lora_rank": 512,
+  "max_position_embeddings": 163840,
+  "model_type": "deepseek_v3",
+  "moe_intermediate_size": 2048,
+  "moe_layer_freq": 1,
+  "n_group": 8,
+  "n_routed_experts": 256,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 61,
+  "num_key_value_heads": 128,
+  "num_nextn_predict_layers": 1,
+  "pretraining_tp": 1,
+  "q_lora_rank": 1536,
+  "qk_head_dim": 192,
+  "qk_nope_head_dim": 128,
+  "qk_rope_head_dim": 64,
+  "rms_norm_eps": 1e-06,
+  "rope_interleave": true,
+  "rope_scaling": {
+    "beta_fast": 32.0,
+    "beta_slow": 1.0,
+    "factor": 40.0,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "rope_type": "yarn",
+    "type": "yarn"
+  },
+  "rope_theta": 10000,
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sigmoid",
+  "seq_aux": true,
+  "tie_word_embeddings": false,
+  "topk_group": 4,
+  "topk_method": "noaux_tc",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0",
+  "use_cache": true,
+  "v_head_dim": 128,
+  "vocab_size": 128815
+}

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