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@@ -23,6 +23,12 @@ quantized_by: dfurman
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  This model is a finetune of `MaziyarPanahi/calme-2.4-rys-78b` on 1.5k rows of the `mlabonne/orpo-dpo-mix-40k` dataset.
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62afc20ca5bd7cef3e1ab3f4/NG5WGL0ljzLsNhSBRVqnD.png)
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62afc20ca5bd7cef3e1ab3f4/Zhk5Bpr1I2NrzX98Bhtp8.png)
@@ -57,7 +63,7 @@ else:
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  attn_implementation = "eager"
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  torch_dtype = torch.float16
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- # quantize if necessary
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  # bnb_config = BitsAndBytesConfig(
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  # load_in_4bit=True,
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  # bnb_4bit_quant_type="nf4",
@@ -84,6 +90,30 @@ pipeline = transformers.pipeline(
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  ### Example 1
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  ```python
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  question = """The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning.
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  They sold 93 loaves in the morning and 39 loaves in the afternoon.
@@ -114,7 +144,7 @@ print(outputs[0]["generated_text"][len(prompt):])
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  |3|Adjust for returns|Add returned loaves to remaining|74|
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  ```
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- ### Example 2
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  ```python
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  question = "What's a good recipe for a spicy margarita?"
 
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  This model is a finetune of `MaziyarPanahi/calme-2.4-rys-78b` on 1.5k rows of the `mlabonne/orpo-dpo-mix-40k` dataset.
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+ It was trained as a generalist language model supporting a variety of text generation use cases, including agentic capabilities, roleplaying, reasoning, multi-turn conversation, long context coherence, and more.
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+
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+ ## 🚅 Training
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+
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+ Here are a few visualizations of the finetune run:
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+
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62afc20ca5bd7cef3e1ab3f4/NG5WGL0ljzLsNhSBRVqnD.png)
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62afc20ca5bd7cef3e1ab3f4/Zhk5Bpr1I2NrzX98Bhtp8.png)
 
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  attn_implementation = "eager"
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  torch_dtype = torch.float16
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+ # # quantize if necessary
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  # bnb_config = BitsAndBytesConfig(
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  # load_in_4bit=True,
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  # bnb_4bit_quant_type="nf4",
 
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  ### Example 1
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+ ```python
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+ question = "Is the number 9.11 larger than 9.9?"
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+
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+ messages = [
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+ {"role": "system", "content": "You are a helpful assistant that thinks step by step."},
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+ {"role": "user", "content": question},
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+ ]
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+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ # print("***Prompt:\n", prompt)
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+
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+ outputs = pipeline(
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+ prompt, max_new_tokens=1000, do_sample=True, temperature=0.01, top_k=50, top_p=0.95
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+ )
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+ print("***Generation:")
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+ print(outputs[0]["generated_text"][len(prompt) :])
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+ ```
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+
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+ ```
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+ ***Generation:
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+ To compare these two numbers, it's important to look at their decimal places after the whole number part, which is 9 in both cases. Comparing the tenths place, 9.11 has a '1' and 9.9 has a '9'. Since '9' is greater than '1', 9.9 is larger than 9.11.
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+ ```
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+
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+ ### Example 2
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+
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  ```python
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  question = """The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning.
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  They sold 93 loaves in the morning and 39 loaves in the afternoon.
 
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  |3|Adjust for returns|Add returned loaves to remaining|74|
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  ```
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+ ### Example 3
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  ```python
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  question = "What's a good recipe for a spicy margarita?"