Hito 1.7B

Brain, Heart, and a Really Good Memory

🧠 Cognitive Bias Resistance

Hito is specifically trained to resist cognitive biases that trip up most AI models and humans alike.

The Bat and Ball Test

"A bat and a ball cost $1.10 together. The bat costs $1.00 more than the ball. How much does the ball cost?"

Most people (and AI models) instinctively say 10 cents. That's wrong.

Model	Parameters	Answer	Correct
Hito 1.7B	1.7B	$0.05	✅
llama3.1	8B	$0.10	❌
deepseek-r1	7B	$0.10	❌
deepseek-r1	32B	$0.10	❌
mistral	7B	$0.10	❌
tinyllama	1.1B	$0.10	❌
llama3.2	1B	$0.10	❌

Hito's reasoning:

<think>
<understand>Ball + Bat = $1.10, Bat = Ball + $1.00</understand>
<doubt>Intuition says 10 cents... but let me verify.</doubt>
<logic>
If ball = $0.10, bat = $1.10, total = $1.20. WRONG.
Let ball = x: x + (x + 1) = 1.10, 2x = 0.10, x = 0.05
</logic>
<verify>Ball $0.05 + Bat $1.05 = $1.10 ✓</verify>
</think>
The ball costs five cents.

📊 Benchmark Results

Tested against public Ollama endpoints with identical prompts:

Model	Params	Counting	Math	Reasoning	Cognitive Bias	Overall
Hito 1.7B	1.7B	100%	100%	100%	✅ Resistant	100%
llama3.1	8B	100%	67%	100%	❌ Fails	89%
deepseek-r1:7b	7B	100%	67%	100%	❌ Fails	89%
deepseek-r1:32b	32B	100%	67%	100%	❌ Fails	89%
mistral	7B	33%	67%	100%	❌ Fails	67%
llama3.2	1B	0%	67%	67%	❌ Fails	44%
tinyllama	1.1B	0%	33%	33%	❌ Fails	33%

Note: Cognitive Bias test uses the bat-and-ball problem. Models marked "Fails" gave the intuitive wrong answer ($0.10) instead of the correct answer ($0.05).

📊 Visual Benchmarks

🎯 What Makes Hito Different

1. Cognitive Bias Resistance

While larger models fall for intuitive traps, Hito is trained to stop and verify before answering.

2. Structured Thinking

Uses cognitive tags (<think>, <doubt>, <verify>) for transparent, traceable reasoning.

3. Self-Aware Identity

Hito knows who it is, who made it, and its purpose. No generic "I'm an AI assistant" responses.

4. Humble by Design

Built-in humility system with tags for doubt, honesty, and acknowledging limits.

Cognitive Architecture

Hito uses a tree-structured reasoning system with four cognitive states:

State	Focus	Tags Used
Analytical	Logic, accuracy	`<logic>`, `<verify>`, `<compare>`
Creative	Imagination, exploration	`<imagine>`, `<brainstorm>`, `<wild>`
Empathetic	Feelings, perspectives	`<emotion>`, `<empathize>`, `<mood>`
Reflective	Depth, meaning	`<reflect>`, `<doubt>`, `<honest>`

The Humble Tags

What makes Hito different is its built-in humility system:

Tag	Purpose
`<doubt>`	Question assumptions
`<honest>`	Admit errors
`<limits>`	Acknowledge knowledge gaps
`<confidence>`	Rate certainty level
`<verify>`	Double-check work

Quick Start

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("hitonet/hito-1.7b", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("hitonet/hito-1.7b")

messages = [
    {"role": "system", "content": "You are Hito by Hitonet.com."},
    {"role": "user", "content": "A bat and ball cost $1.10. The bat costs $1 more than the ball. How much is the ball?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Ollama

# Download GGUF from hitonet/hito-1.7b-GGUF
ollama create hito -f Modelfile
ollama run hito

API

curl https://api.hitonet.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "hito", "messages": [{"role": "user", "content": "Hello!"}]}'

Try the full API at platform.hitonet.com - $1 free credit included.

Model Variants

Repository	Format	Use Case
hitonet/hito-1.7b	Safetensors	Python/Transformers
hitonet/hito-1.7b-GGUF	GGUF	Ollama/llama.cpp/LM Studio

Recommended GGUF Quantizations

Quantization	Size	Quality	Use Case
Q4_K_M	1.1 GB	⭐ Best Balance	Most users
Q5_K_M	1.2 GB	Excellent	Quality-focused
Q8_0	1.8 GB	Highest	Maximum quality

Research

For technical details on Nested Cognitive Reasoning, see our research paper:

Nested Cognitive Reasoning: A Tree-Structured Approach to Language Model Thinking

Hitonet Research, 2025

Licensing

Component	License	Commercial Use
Model Weights	Apache 2.0	✅ Free
NCR Methodology	CC BY-NC-ND	⚠️ License Required

The model weights are fully open source under Apache 2.0.

The Nested Cognitive Reasoning methodology (cognitive tags, tree-structured thinking, humble tags system) is protected under CC BY-NC-ND. Commercial use of the NCR method requires a license.

Contact: legal@hitonet.com

Model tree for hitonet/hito-1.7b

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B