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README.md

@@ -10,8 +10,11 @@ Model details:
  - **Attention Head Count**: 4
  - **Residual Stream Size**: 256
  - **Context Length**: 256
- - **Tokens Trained on**: 419,706,880
+ - **Tokens Trained on**: 419,649,024
 
+Training Score During Training
+
+[score.png](score.png)
 
 ```py
 from transformers import pipeline
@@ -19,7 +22,8 @@ from transformers import pipeline
 pipe = pipeline(
     "text-generation", model="bart1259/MiniCOTMath"
 )
-print(pipe("Input: (5 + 5)\n", max_new_tokens=100)[0]["generated_text"])
+print(pipe("Input: (5 + 5)
+", max_new_tokens=100)[0]["generated_text"])
 ```
 
 Outputs:
@@ -59,7 +63,8 @@ class StopCriteria(StoppingCriteria):
     def __iter__(self):
         yield self
 
-prompt = "Input: (3 * (5 + 5))\n"
+prompt = "Input: (5 + 5)
+"
 
 tokenizer = AutoTokenizer.from_pretrained("bart1259/MiniCOTMath")
 model = AutoModelForCausalLM.from_pretrained("bart1259/MiniCOTMath").cuda()
@@ -81,18 +86,14 @@ _ = model.generate(
 
 Outputs:
 ```
-Input: (3 * (5 + 5))
+Input: (5 + 5)
 
 Step 1:
-(3 * (5 + 5))
+(5 + 5)
 (5 + 5) = 10
 
 Step 2:
-(3 * 10)
-(3 * 10) = 30
-
-Step 3:
-30
-Final Result: 30
+10
+Final Result: 10
 <end>
 ```

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b0e7b2d91eb4e7ea8bdc4b94640382a0af079b8e8e9974b801c9c48fdf4af330
+oid sha256:80470ec0e1be3e0e2ae474707fdd150d02d666915dfaa5d54528418c03e7da66
 size 25352072