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Model: dfurman/Llama-3-8B-Orpo-v0.1 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: llama3
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library_name: transformers
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tags:
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- orpo
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- llama 3
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- rlhf
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- sft
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base_model:
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- meta-llama/Meta-Llama-3-8B
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datasets:
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- mlabonne/orpo-dpo-mix-40k
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model-index:
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- name: Llama-3-8B-Orpo-v0.1
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 30.0
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 13.77
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 3.78
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 1.57
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 2.73
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 14.23
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dfurman/Llama-3-8B-Orpo-v0.1
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name: Open LLM Leaderboard
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---
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# dfurman/Llama-3-8B-Orpo-v0.1
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This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 4k samples of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k).
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It's a successful fine-tune that follows the ChatML template!
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## 🔎 Application
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This model uses a context window of 8k. It was trained with the ChatML template.
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## 🏆 Evaluation
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### Open LLM Leaderboard
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| Model ID | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: | --------: | --------: |
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| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Meta-Llama-3-8B-Instruct) | 66.87 | 60.75 | 78.55 | 67.07 | 51.65 | 74.51 | 68.69 |
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| [**dfurman/Llama-3-8B-Orpo-v0.1**](https://huggingface.co/dfurman/Llama-3-8B-Orpo-v0.1) [📄](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__Llama-3-8B-Orpo-v0.1) | **64.67** | **60.67** | **82.56** | **66.59** | **50.47** | **79.01** | **48.75** |
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| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Meta-Llama-3-8B) | 62.35 | 59.22 | 82.02 | 66.49 | 43.95 | 77.11 | 45.34 |
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## 📈 Training curves
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You can find the experiment on W&B at [this address](https://wandb.ai/dryanfurman/huggingface/runs/uvr916mv?nw=nwuserdryanfurman).
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## 💻 Usage
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<details>
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<summary>Setup</summary>
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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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if torch.cuda.get_device_capability()[0] >= 8:
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!pip install -qqq flash-attn
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attn_implementation = "flash_attention_2"
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torch_dtype = torch.bfloat16
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else:
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attn_implementation = "eager"
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torch_dtype = torch.float16
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model = "dfurman/Llama-3-8B-Orpo-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={
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"torch_dtype": torch_dtype,
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"device_map": "auto",
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"attn_implementation": attn_implementation,
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}
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)
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```
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</details>
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### Run
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```python
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Tell me a recipe for a spicy margarita."},
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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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outputs = pipeline(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print("***Generation:\n", outputs[0]["generated_text"][len(prompt):])
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```
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<details>
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<summary>Output</summary>
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```
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"""***Prompt:
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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Tell me a recipe for a spicy margarita.<|im_end|>
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<|im_start|>assistant
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***Generation:
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Sure! Here's a recipe for a spicy margarita:
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Ingredients:
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- 2 oz silver tequila
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- 1 oz triple sec
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- 1 oz fresh lime juice
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- 1/2 oz simple syrup
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- 1/2 oz fresh lemon juice
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- 1/2 tsp jalapeño, sliced (adjust to taste)
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- Ice cubes
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- Salt for rimming the glass
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Instructions:
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1. Prepare the glass by running a lime wedge around the rim of the glass. Dip the rim into a shallow plate of salt to coat.
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2. Combine the tequila, triple sec, lime juice, simple syrup, lemon juice, and jalapeño slices in a cocktail shaker.
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3. Add ice cubes to the cocktail shaker and shake vigorously for 30 seconds to 1 minute.
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4. Strain the cocktail into the prepared glass.
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5. Garnish with a lime wedge and jalapeño slice.
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Enjoy! This spicy margarita has a nice balance of sweetness and acidity, with a subtle heat from the jalapeño that builds gradually as you sip."""
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```
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</details>
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__Llama-3-8B-Orpo-v0.1)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |11.01|
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|IFEval (0-Shot) |30.00|
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|BBH (3-Shot) |13.77|
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|MATH Lvl 5 (4-Shot)| 3.78|
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|GPQA (0-shot) | 1.57|
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|MuSR (0-shot) | 2.73|
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|MMLU-PRO (5-shot) |14.23|
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