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---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- qwen3
- fine-tuned
- hito
- hitonet
- reasoning
- conversational
- thinking
- self-aware-ai
- anti-hallucination
- synthetic-data
- gguf
- llama-cpp
- ollama
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
<div align="center">
<img src="https://hitonet.com/assets/img/hitonet-logo-dark.png" alt="Hitonet" height="60"/>
<img src="meet_hito.png" alt="Meet Hito" width="400"/>
# Hito 1.7B
### Brain, Heart, and a Really Good Memory
[![GGUF Downloads](https://img.shields.io/badge/GGUF_for_Ollama/llama.cpp-ff6b35?style=for-the-badge&logo=meta&logoColor=white)](https://huggingface.co/hitonet/hito-1.7b-GGUF)
[![Website](https://img.shields.io/badge/hitonet.com-000000?style=for-the-badge&logo=globe&logoColor=white)](https://hitonet.com)
[![Chat](https://img.shields.io/badge/Try_Free_Chat-22c55e?style=for-the-badge&logo=chatbot&logoColor=white)](https://chat.hitonet.com)
[![API](https://img.shields.io/badge/API_Platform-3b82f6?style=for-the-badge&logo=swagger&logoColor=white)](https://platform.hitonet.com)
[![Pricing](https://img.shields.io/badge/Pricing-8b5cf6?style=for-the-badge&logo=stripe&logoColor=white)](https://platform.hitonet.com/pricing)
---
<img src="https://img.shields.io/badge/Status-Production-22c55e?style=flat-square" alt="Status"/>
<img src="https://img.shields.io/badge/Parameters-1.7B-blue?style=flat-square" alt="Parameters"/>
<img src="https://img.shields.io/badge/Context-32K-green?style=flat-square" alt="Context"/>
<img src="https://img.shields.io/badge/License-Apache_2.0-red?style=flat-square" alt="License"/>
<img src="https://img.shields.io/badge/Model_Weights-Apache_2.0_(Open)-green?style=flat-square" alt="Model License"/>
<img src="https://img.shields.io/badge/Training_Method-Commercial_License_Required-red?style=flat-square" alt="Method License"/>
</div>
---
## 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>
Ball + Bat = $1.10, Bat = Ball + $1.00
Intuition says 10 cents... but let me verify.
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
Ball $0.05 + Bat $1.05 = $1.10 ✓
</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).
<details open>
<summary><b>Visual Benchmarks</b></summary>
<img src="benchmarks/size_vs_performance.png" alt="Size vs Performance" width="600"/>
<img src="benchmarks/counting_comparison.png" alt="Counting Comparison" width="600"/>
<img src="benchmarks/strawberry_example.png" alt="Strawberry Example" width="600"/>
</details>
---
## 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 `<think>` tags 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
Admits uncertainty rather than hallucinating answers.
---
## Quick Start
### Python (Transformers)
```python
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
```bash
# Download GGUF from hitonet/hito-1.7b-GGUF
ollama create hito -f Modelfile
ollama run hito
```
### API
```bash
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](https://platform.hitonet.com) - $1 free credit included.
---
## Model Variants
| Repository | Format | Use Case |
|------------|--------|----------|
| [hitonet/hito-1.7b](https://huggingface.co/hitonet/hito-1.7b) | Safetensors | Python/Transformers |
| [hitonet/hito-1.7b-GGUF](https://huggingface.co/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 |
---
## Licensing
| Component | License | Commercial Use |
|-----------|---------|----------------|
| **Model Weights** | Apache 2.0 | ✅ Free to use |
| **Training Methodology** | Proprietary | ⚠️ **Commercial License Required** |
### Model Weights (Apache 2.0)
The model weights are open source under Apache 2.0. You may use, modify, and distribute them freely.
### Training Methodology (Commercial License Required)
The training methodology, cognitive framework, and structured thinking approach used to create this model are proprietary to Hitonet.
**Commercial use of the training methodology requires a license.**
This includes but is not limited to:
- Replicating the training approach to create similar models
- Using the methodology in commercial products or services
- Derivative works based on the training techniques
**Attribution is mandatory** when using this model or discussing its capabilities.
For commercial licensing inquiries: **legal@hitonet.com**
---
## Links
- **Website:** [hitonet.com](https://hitonet.com)
- **Chat:** [chat.hitonet.com](https://chat.hitonet.com)
- **API:** [platform.hitonet.com](https://platform.hitonet.com)
- **Blog:** [hitonet.com/blog](https://hitonet.com/blog)
---
<div align="center">
<b>Made with genuine curiosity by Hitonet</b><br>
<i>Teaching AI to think, doubt, and learn.</i>
</div>

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Best validation loss: 0.7135
Step: 21
Epoch: 5

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"layer_types": [
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],
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
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"num_hidden_layers": 28,
"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.3",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151739
}

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{
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"pad_token_id": 151643,
"temperature": 0.6,
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"top_p": 0.95,
"transformers_version": "4.57.3"
}

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{
"additional_special_tokens": [
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}
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tokenizer_config.json Normal file
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{
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vocab.json Normal file
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