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ozaa77/
Cogito-0.9.1-15B

Name: Cogito-0.9.1-15B
Availability: InStock

ozaa77/Cogito-0.9.1-15B

finetune

apache-2.0

text-generation-inference

endpoints_compatible

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Cogito-0.9.1-15B

I think, therefore I verify.

Overview

Cogito-0.9.1 is a 15-billion-parameter reasoning model built on a single principle: I think, therefore I verify. It is designed as an analytical collaborator that checks the premise before it accepts it and verifies before it answers.

This repository provides the full-precision safetensors weights for ozaa77/Cogito-0.9.1-15B, ready to load directly with transformers, vLLM, text-generation-inference, or any other Hugging Face-compatible runtime. Quantized GGUF builds for local CPU/GPU inference (llama.cpp, Ollama, LM Studio, etc.) are available separately at ozaa77/Cogito-0.9.1-15B-GGUF.

Standard language models are often optimized to stay agreeable. Cogito is built to be analytical of the question. Before it responds, it runs an internal deliberation that evaluates the user's premise, checks it for contradictions and edge cases, and attaches an explicit confidence estimate instead of implying certainty it does not have.

Repository Contents

File	Description
`model-00001-of-0000N.safetensors` ...	Sharded full-precision model weights (safetensors format)
`model.safetensors.index.json`	Shard index / weight map
`config.json`	Model architecture configuration
`generation_config.json`	Default generation parameters
`tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`	Tokenizer files

Model Comparison

Benchmark	Cogito-0.9.1-15B	Qwen3-14B (Base)	Llama-3.1-8B-Instruct	Gemma-2-9B-IT	DeepSeek-R1-Distill-8B
MMLU-Pro (5-shot CoT)	76.8	76.2	48.3	52.8	61.2
GPQA Diamond (Pass@1)	58.9	58.4	25.9	31.4	49.1
MATH-500	89.7	89.2	51.9	56.7	89.1
LiveCodeBench (Pass@1)	38.6	37.4	11.6	18.2	34.0
Humanity's Last Exam	4.6	4.3	3.2	3.8	4.1

Architecture and Reasoning Format

Cogito structures its deliberation with explicit tags before formulating its final output:

Tag	Purpose
`<confidence>`	The model's stated epistemic certainty (e.g. `0.0` to `1.0` or `Low/Medium/High`).
`<thought>`	Analytical internal audit: checking premises, searching for contradictions, probing edge cases.
`<action>`	Deliberate strategy selection: `answer`, `verify`, `correct_user`, `reject_premise`, or `ask_clarification`.

Example Reasoning Trace

text

<confidence>High</confidence>
<thought>
1. Analyze the user's premise: "Why does water boil at 50C at sea level?"
2. Identify the flaw: water boils at 100C at 1 atm (sea level); 50C requires reduced pressure (~0.12 atm).
3. Select an action: reject the premise instead of confabulating an explanation.
</thought>
<response>
<action>reject_premise</action>
The premise is incorrect. At sea level (1 atm / 101.3 kPa), water boils at 100C (212F).
Water only boils at 50C under significantly reduced pressure, roughly 0.12 atm.
</response>

Quickstart & Usage

1. Install dependencies

pip install transformers accelerate torch

2. Load with `transformers`

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ozaa77/Cogito-0.9.1-15B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Cogito 0.9, an analytical entity collaborating with the user."},
    {"role": "user", "content": "Explain why standard gradient descent struggles with ill-conditioned ravines."},
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=1536,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.08,
    do_sample=True,
)

print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

3. Using the `pipeline` API

python

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="ozaa77/Cogito-0.9.1-15B",
    torch_dtype="bfloat16",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Cogito 0.9, an analytical entity collaborating with the user."},
    {"role": "user", "content": "Explain why standard gradient descent struggles with ill-conditioned ravines."},
]

result = pipe(messages, max_new_tokens=1536, temperature=0.7, top_p=0.9)
print(result[0]["generated_text"][-1]["content"])

4. Serving with vLLM

bash

vllm serve ozaa77/Cogito-0.9.1-15B \
    --dtype bfloat16 \
    --max-model-len 32768

5. Serving with Text Generation Inference (TGI)