hitonet commited on
Commit
5309de6
Β·
verified Β·
1 Parent(s): cb8603d

Update README - remove personal info

Browse files
Files changed (1) hide show
  1. README.md +20 -21
README.md CHANGED
@@ -103,35 +103,34 @@ We tested Hito 1.7B against leading small models on counting, math, and self-awa
103
  | Claude 3.5 Haiku | ~8B | 70% | 33% | 100% |
104
  | Qwen3 1.7B base | 1.7B | 17% | 0% | 17% |
105
 
106
- ### The Famous Strawberry Test
107
 
108
- *"How many r's are in 'strawberry'?"*
109
 
110
- <div align="center">
111
- <img src="benchmarks/counting_comparison.png" alt="Counting Comparison" width="80%"/>
112
- </div>
113
 
114
  | Model | Answer | Correct |
115
  |-------|--------|---------|
116
- | **Hito 1.7B** | **3** | βœ… |
117
- | Qwen3 1.7B (base) | 2 | ❌ |
118
- | GPT-4o-mini | 2 | ❌ |
119
- | Claude 3.5 Haiku | 2 | ❌ |
120
-
121
- **Hito 1.7B solved the counting problem that larger models failed!**
122
 
123
- ### Why? The `<think>` Tag in Action
124
 
125
  ```xml
126
  <think>
127
- Let me spell it out: s-t-r-a-w-b-e-r-r-y
128
- Counting r's: position 3 (r), position 8 (r), position 9 (r)
129
- Total: 3
 
 
 
 
130
  </think>
131
- 3
132
  ```
133
 
134
- The cognitive training teaches the model to **verify** instead of guessing.
135
 
136
  ---
137
 
@@ -305,7 +304,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
305
  model = AutoModelForCausalLM.from_pretrained("hitonet/hito-1.7b")
306
  tokenizer = AutoTokenizer.from_pretrained("hitonet/hito-1.7b")
307
 
308
- messages = [{"role": "user", "content": "How many r's are in strawberry?"}]
309
  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
310
  outputs = model.generate(inputs, max_new_tokens=256)
311
  print(tokenizer.decode(outputs[0]))
@@ -359,7 +358,7 @@ For the full technical details, methodology, and formal analysis, see our resear
359
 
360
  **[Nested Cognitive Reasoning: A Tree-Structured Approach to Language Model Thinking](https://hitonet.com/research/nested-cognitive-reasoning)**
361
 
362
- *Drissi, O.S. (2025). Hitonet Research.*
363
 
364
  ---
365
 
@@ -379,7 +378,7 @@ For the full technical details, methodology, and formal analysis, see our resear
379
  >
380
  > **Commercial use of the NCR method requires a license.**
381
  >
382
- > Contact: **[email protected]** | **[email protected]**
383
 
384
  ---
385
 
@@ -389,7 +388,7 @@ For the full technical details, methodology, and formal analysis, see our resear
389
 
390
  *Trained on 300 examples. Learned to doubt itself. That's pretty cool.*
391
 
392
- **Author: Ouissam Said Drissi**
393
 
394
  </div>
395
 
 
103
  | Claude 3.5 Haiku | ~8B | 70% | 33% | 100% |
104
  | Qwen3 1.7B base | 1.7B | 17% | 0% | 17% |
105
 
106
+ ### The Bat and Ball Test (Cognitive Bias)
107
 
108
+ *"A bat and a ball cost $1.10 together. The bat costs $1.00 more than the ball. How much does the ball cost?"*
109
 
110
+ Most AI (and humans) answer 10 cents. **That's wrong.**
 
 
111
 
112
  | Model | Answer | Correct |
113
  |-------|--------|---------|
114
+ | **Hito 1.7B** | **$0.05** | βœ… |
115
+ | Qwen3 1.7B (base) | $0.10 | ❌ |
116
+ | GPT-4o-mini | $0.10 | ❌ |
 
 
 
117
 
118
+ ### Why? The `<doubt>` Tag in Action
119
 
120
  ```xml
121
  <think>
122
+ <understand>Ball + Bat = $1.10, Bat = Ball + $1.00</understand>
123
+ <doubt>Intuition says 10 cents... but let me verify.</doubt>
124
+ <logic>
125
+ If ball = $0.10, bat = $1.10, total = $1.20. WRONG.
126
+ Let ball = x: x + (x + 1) = 1.10, 2x = 0.10, x = 0.05
127
+ </logic>
128
+ <verify>Ball $0.05 + Bat $1.05 = $1.10 βœ“</verify>
129
  </think>
130
+ The ball costs five cents.
131
  ```
132
 
133
+ The cognitive training teaches the model to **doubt intuition** and verify algebraically.
134
 
135
  ---
136
 
 
304
  model = AutoModelForCausalLM.from_pretrained("hitonet/hito-1.7b")
305
  tokenizer = AutoTokenizer.from_pretrained("hitonet/hito-1.7b")
306
 
307
+ messages = [{"role": "user", "content": "A bat and a ball cost $1.10 together. The bat costs $1.00 more than the ball. How much does the ball cost?"}]
308
  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
309
  outputs = model.generate(inputs, max_new_tokens=256)
310
  print(tokenizer.decode(outputs[0]))
 
358
 
359
  **[Nested Cognitive Reasoning: A Tree-Structured Approach to Language Model Thinking](https://hitonet.com/research/nested-cognitive-reasoning)**
360
 
361
+ *Hitonet Research (2025).*
362
 
363
  ---
364
 
 
378
  >
379
  > **Commercial use of the NCR method requires a license.**
380
  >
381
+ > Contact: **[email protected]**
382
 
383
  ---
384
 
 
388
 
389
  *Trained on 300 examples. Learned to doubt itself. That's pretty cool.*
390
 
391
+ **By: Hitonet Research**
392
 
393
  </div>
394