Initial upload of fine‑tuned Gemma + custom tokenizer

README.md

@@ -71,16 +71,16 @@ with torch.no_grad():
 Output:
 ```
 Top 10 probabilities for first output token:
- 1. 'Tokyo' -> 0.9792
- 2. 'Tok' -> 0.0059
- 3. '東京' -> 0.0028
+ 1. 'Tokyo' -> 0.9764
+ 2. 'Tok' -> 0.0070
+ 3. '東京' -> 0.0026
  4. 'Ky' -> 0.0019
- 5. 'T' -> 0.0011
- 6. ' Tokyo' -> 0.0011
- 7. 'Osaka' -> 0.0009
- 8. 'To' -> 0.0008
- 9. 'Toy' -> 0.0006
-10. 'Nag' -> 0.0005
+ 5. 'T' -> 0.0014
+ 6. ' Tokyo' -> 0.0014
+ 7. 'To' -> 0.0011
+ 8. 'Osaka' -> 0.0009
+ 9. 'Toy' -> 0.0007
+10. 'tok' -> 0.0005
 ```
 
 Great! Almost all of the probability mass is on the correct answer, Tokyo.
@@ -110,10 +110,10 @@ for i in range(n_gens):
 
 Outputs:
 ```
-7 Wonders: Duel
-Dominion
-Terraforming Mars
-Agricola: All Creatures Big and Small
+Catan: Rivals for Catan
+Gloomhaven
+Great Western Trail
+Azul
 ```
 Not too bad!
 
@@ -135,12 +135,48 @@ for i in range(n_gens):
 ```
 Output:
 ```
-Navy Blue
-White
-light green
-yellow
+Deep Sea Blue
+Gray#222222
+Gold, Red, Black
+I can’t believe we’re already talking about color theory. How is this possible? Can time go any faster? Also how does your body    
 ```
 
+By default, the model is only trained to do 1) either emulate outputs if examples are provided, or 2) generate data based on the description. Because of this, the model always expects EITHER a description OR examples. If you want it to act slightly more like an instruction following chat model, you can add a description such as the following:
+
+```
+messages = [
+    {"role": "description", "content": "You are a helpful assistant who outputs the requested content."},
+    {"role": "input", "content": "A poem about a shark"},
+]
+```
+To generate:
+```
+formatted_prompt = tokenizer.messages_to_text(messages, start_generation=True)
+n_gens = 4
+inputs = tokenizer([formatted_prompt] * n_gens, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=40, stop_strings=["<end_of_turn>"], tokenizer=tokenizer)
+for i in range(n_gens):
+    print(f"Generation {i}:")
+    print(tokenizer.decode(outputs[i][inputs["input_ids"][i].shape[0]:], skip_special_tokens=True))
+```
+
+Some example generations:
+```
+Generation 0:
+A deep-sea creature, silent and fierce, Shivers through water, its body sleek. Its jaws, a vice, its eyes cold steel, The shark moves with grace, never to feel.
+of power and danger,
+Generation 1:
+The great white shark lurks in the deep, with teeth so sharp, it could cut a whale in half. Its dorsal fin slices through the water, like a knife through butter, and its tail
+Generation 2:
+The shark swam in the sea, With a toothy grin, as if it could be glee. It was the top of the food chain, The apex of the sea's terrain. With sleek  
+Generation 3:
+I am a gentle, tranquil wave, gliding smoothly across the ocean's expanse. Yet deep within me lies a secret, a hidden power, a creature of the sea, fierce and agile. It
+```
+
+
+
+
 Finally, let's look at a synthetic data generation task. For example, maybe we want to generate situations to do social reasoning over, along with whether or not they are awkward. When there are multiple variables to condition on or generat, the model is used to json format.
 
 Input:
@@ -172,13 +208,10 @@ for i in range(n_gens):
 ```
 Output:
 ```
-{"situation": "Noticing that you've been holding the wrong end of your toothbrush.", "is_awkward": true}
-
-{"situation": "Your teacher gives you extra credit for participating in class.", "is_awkward": false}
-
-{"situation": "Your friend doesn't recognize you because of your new look.", "is_awkward": true}
-
-{"situation": "Your friend comes over and asks you to help them move some furniture, but you have other plans.", "is_awkward": true}
+{"situation": "While walking on the street, someone waves and smiles at you, but you don't know them.", "is_awkward": false}                        
+{"situation": "Taking a cab and giving the driver wrong directions.", "is_awkward": true}                                                           
+{"situation": "Being told that an individual you've had a long-term crush on is also crushing on someone else.", "is_awkward": true}                
+{"situation": "Watching a loved one get proposed to.", "is_awkward": false}  
 ```
 
 A few tips and tricks: