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--- |
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tags: |
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- merge |
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- mergekit |
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- cognitivecomputations/dolphin-2.9-llama3-8b |
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- abacusai/Llama-3-Smaug-8B |
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- meta-llama/Meta-Llama-3-8B |
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base_model: |
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- cognitivecomputations/dolphin-2.9-llama3-8b |
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- abacusai/Llama-3-Smaug-8B |
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- meta-llama/Meta-Llama-3-8B |
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license: apache-2.0 |
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--- |
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# 💦 aqua-smaug-0.3-8B 🐉 |
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aqua-smaug-0.3-8B is a merge of the following models using [Mergekit](https://github.com/arcee-ai/mergekit): |
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* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) |
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* [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) |
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* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) |
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## 🧩 Configuration |
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```yamlname: aqua-smaug-0.3-8B |
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models: |
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- model: cognitivecomputations/dolphin-2.9-llama3-8b |
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- model: abacusai/Llama-3-Smaug-8B |
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- model: meta-llama/Meta-Llama-3-8B |
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merge_method: model_stock |
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base_model: abacusai/Llama-3-Smaug-8B |
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dtype: bfloat16 |
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``` |
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## Eval Results |
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|Benchmark| Model |winogrande| arc |gsm8k|mmlu|truthfulqa|hellaswag|Average| |
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|---------|--------------------------------------------------------------------|---------:|----:|----:|---:|---------:|--------:|------:| |
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|openllm |[aqua-smaug-0.3-8B](https://huggingface.co/saucam/aqua-smaug-0.3-8B)| 77.11|62.37|76.19| 66| 53.7| 83.02| 69.73| |
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Detailed Results: https://github.com/saucam/model_evals/tree/main/saucam/aqua-smaug-0.3-8B |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "saucam/aqua-smaug-0.3-8B" |
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messages = [{"role": "user", "content": "A carnival snack booth made $50 selling popcorn each day. It made three times as much selling cotton candy. For a 5-day activity, the booth has to pay $30 rent and $75 for the cost of the ingredients. How much did the booth earn for 5 days after paying the rent and the cost of ingredients? How much did the booth make selling cotton candy each day?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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output |
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``` |
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Loading checkpoint shards: 100%|███████████████████████████████████████████████████| 2/2 [00:27<00:00, 13.83s/it] |
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Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. |
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<|begin_of_text|><|start_header_id|>user<|end_header_id|> |
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A carnival snack booth made $50 selling popcorn each day. It made three times as much selling cotton candy. For a 5-day activity, the booth has to pay $30 rent and $75 for the cost of the ingredients. How much did the booth earn for 5 days after paying the rent and the cost of ingredients? How much did the booth make selling cotton candy each day?<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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The carnival snack booth made $50 selling popcorn each day. Since it made three times as much selling cotton candy, it made $50 * 3 = $150 each day selling cotton candy. |
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For a 5-day activity, the booth made $50 * 5 = $250 selling popcorn and $150 * 5 = $750 selling cotton candy. |
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The booth has to pay $30 rent and $75 for the cost of the ingredients for 5 days, which is a total of $30 + $75 = $105. |
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After paying the rent and the cost of ingredients, the booth earned $250 + $750 - $105 = $895 for 5 days. |
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Therefore, the booth made $150 each day selling cotton candy. |
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So, the total amount earned by selling popcorn is $250 and by selling cotton candy is $750. After deducting the rent and cost of ingredients, the booth earned a total of $895 for the 5-day activity. |
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Hope this helps! Let me know if you have any more questions. 😊 |
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### References |
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- [Carnival Booth Earnings Calculation](https://www.calculator.net/calculators/math/equation-calculator.html) (for verifying calculations) |
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- [Cotton Candy |
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``` |
