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README.md

@@ -1,3 +1,87 @@
----
-license: mit
----
+---
+license: mit
+---
+
+Chain of Thought (CoT) transformer model trained to do multi-step integer arithmetic.
+
+```py
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation", model="bart1259/MiniCOTMath"
+)
+print(pipe("Input: (5 + 5)
+", max_new_tokens=100)[0]["generated_text"])
+```
+
+Outputs:
+```
+Input: (5 + 5)
+
+Step 1:
+(5 + 5)
+(5 + 5) = 10
+
+Step 2:
+10
+Final Result: 10
+<end>
+
+Input: (3 * 8)
+
+Step 1:
+(3 * 8)
+(3
+```
+
+To end at the <end> token, you can setup streaming like:
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, StoppingCriteria
+from transformers import StoppingCriteria
+
+class StopCriteria(StoppingCriteria):
+    def __call__(self, input_ids, scores, **kwargs):
+        generated_text = tokenizer.decode(input_ids[0])
+        return "<end>" in generated_text
+
+    def __len__(self):
+        return 1
+
+    def __iter__(self):
+        yield self
+
+prompt = "Input: (5 + 5)
+"
+
+tokenizer = AutoTokenizer.from_pretrained("bart1259/MiniCOTMath")
+model = AutoModelForCausalLM.from_pretrained("bart1259/MiniCOTMath").cuda()
+
+encoded_input = tokenizer(prompt, return_tensors='pt')
+input_ids=encoded_input['input_ids'].cuda()
+
+streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=False)
+_ = model.generate(
+    input_ids,
+    streamer=streamer,
+    pad_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    temperature=0.25,
+    max_new_tokens=256,
+    stopping_criteria=StopCriteria()
+)
+```
+
+Outputs:
+```
+Input: (5 + 5)
+
+Step 1:
+(5 + 5)
+(5 + 5) = 10
+
+Step 2:
+10
+Final Result: 10
+<end>
+```