You can deploy this model and start earning money today!

unsloth/
cogito-v2-preview-llama-109B-MoE-GGUF

Name: cogito-v2-preview-llama-109B-MoE-GGUF
Availability: InStock

deepcogito/cogito-v2-preview-llama-109B-MoE

Views

No views yet

[!NOTE] Includes Unsloth chat template fixes!
For llama.cpp, use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Logo

Cogito v2 preview - 109B MoE

Blog Post

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 long contexts (upto 10M tokens).

Evaluations

For detailed evaluations, please refer to the Blog Post.

Usage

Here is a snippet below for usage with Transformers:

python

import transformers
import torch

model_id = "deepcogito/cogito-v2-preview-llama-109B-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 (<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-109B-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-109B-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_call>
{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
</tool_call><|eot|>

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:

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|>'

License

This repository and the model weights are licensed under the Llama 4 Community License Agreement (Llama models' default license agreement).

Contact

If you would like to reach out to our team, send an email to contact@deepcogito.com.

unsloth/cogito-v2-preview-llama-109B-MoE-GGUF

Cogito v2 preview - 109B MoE

Evaluations

Usage

Implementing extended thinking

Method 1 - Set enable_thinking=True in the tokenizer

Method 2 - Add a specific system prompt, along with prefilling the response with "<think>\n".

Tool Calling

License

Contact

unsloth/
cogito-v2-preview-llama-109B-MoE-GGUF