HachiML
/

Swallow-MS-7b-v0.1-MathSkill-OpenMath

@@ -1,13 +1,22 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
@@ -35,167 +44,65 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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 ---
 library_name: transformers
+tags:
+  - SkillTree
+  - mistral
+license: apache-2.0
 ---
+# Model Card for SkillTree Enhanced Model
 <!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+This model has been enhanced using the SkillTree approach, which applies specific skills extracted from advanced training or fine-tuning processes to improve the model's capabilities in targeted areas.
+- **Base Model:** (Name of the base model)
+- **Skill Tree Applied:** (Name of the skill tree used for enhancement)
+- **Language(s) (NLP):** Japanese
 ## Model Details
 ## Uses
+This section should describe the intended use cases for the enhanced model. It might include scenarios such as code generation, conversational AI, text summarization, or any other specific tasks the model has been enhanced to perform better. Be sure to include any recommendations or limitations on the model's use.
+```Python
+# Import library
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# Load model
+model_name = "HachiML/Swallow-MS-7b-v0.1-MathSkill-OpenMath"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+# Inference 1
+prompt = "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?\n"
+input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=256, temperature=0.99, top_p=0.95, do_sample=True)
+out = tokenizer.decode(tokens[0], skip_special_tokens=True)
+print(out)
+# Inference 2
+prompt = "ナタリアは4月に48人の友人にクリップを売り、5月にはその半分の数のクリップを売った。ナタリアが4月と5月に売ったクリップの数は？:\n"
+input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=256, temperature=0.99, top_p=0.95, do_sample=True)
+out = tokenizer.decode(tokens[0], skip_special_tokens=True)
+print(out)
+```
+## Response Examples
+Below are examples of responses from the model, demonstrating how the SkillTree enhancement has improved its performance.
+### Example 1:
+- **Input:** Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?\n
+- **Output:** Let's solve this problem using Python code.
+<llm-code>
+friends_sold_in_April = 48
+friends_sold_in_May = friends_sold_in_April / 2
+clips_sold_in_April_and_May = friends_sold_in_April + friends_sold_in_May
+clips_sold_in_April_and_May
+</llm-code>
+<llm-code-output>
+72.0
+</llm-code-output>
+Thus, in April and May, Natalia sold \boxed{72} clips in total.
+### Example 2:
+- **Input:** ナタリアは4月に48人の友人にクリップを売り、5月にはその半分の数のクリップを売った。ナタリアが4月と5月に売ったクリップの数は？:\n
+- **Output:** Let's solve this problem using Python code.
+<llm-code>
+clip_count = 48
+clip_count_sold_4th_month = clip_count
+clip_count_sold_5th_month = clip_count_sold_4th_month / 2
+clip_count_sold_in_both_months = clip_count_sold_4th_month + clip_count_sold_5th_month
+clip_count_sold_in_both_months
+</llm-code>
+<llm-code-output>
+72.0
+</llm-code-output>
+So, the total number of clip sales is \textbf{72}.