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--- |
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language: |
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- en |
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license: mit |
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library_name: transformers |
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tags: |
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- orpo |
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- qwen2 |
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- sft |
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- chatml |
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base_model: |
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- MaziyarPanahi/calme-2.4-rys-78b |
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datasets: |
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- mlabonne/orpo-dpo-mix-40k |
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pipeline_tag: text-generation |
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inference: false |
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model_creator: dfurman |
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quantized_by: dfurman |
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model-index: |
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- name: CalmeRys-78B-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: 81.63 |
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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/CalmeRys-78B-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: 61.92 |
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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/CalmeRys-78B-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: 37.92 |
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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/CalmeRys-78B-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: 20.02 |
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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/CalmeRys-78B-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: 36.37 |
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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/CalmeRys-78B-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: 66.8 |
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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/CalmeRys-78B-Orpo-v0.1 |
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name: Open LLM Leaderboard |
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--- |
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# dfurman/CalmeRys-78B-Orpo-v0.1 |
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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. It was trained as a generalist language model for a variety of text generation use cases, including support of agentic capabilities, roleplaying, reasoning, multi-turn conversations, long context coherence, and more. |
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As of Oct 2024, this is the top ranking model on the [Open LLM Leaderboard](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard) 🏆. |
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Thanks go out to [mlabonne](https://huggingface.co./mlabonne), [MaziyarPanahi](https://hf.xwall.us.kg.m/MaziyarPanahi), et al. for the source dataset and base model. |
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## 🦾 Training |
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You can find the experiment on W&B at this [link](https://wandb.ai/dryanfurman/huggingface/runs/1w50nu70?nw=nwuserdryanfurman). Here are a few visualizations: |
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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 bitsandbytes |
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!huggingface-cli download dfurman/CalmeRys-78B-Orpo-v0.1 |
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``` |
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```python |
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from transformers import AutoTokenizer, BitsAndBytesConfig |
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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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# # 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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# bnb_4bit_compute_dtype=torch_dtype, |
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# bnb_4bit_use_double_quant=True, |
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# ) |
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model = "dfurman/CalmeRys-78B-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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# "quantization_config": bnb_config, |
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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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### 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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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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outputs = pipeline( |
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prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, 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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***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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### Example 2 |
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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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A grocery store then returned 6 unsold loaves back to the bakery. |
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How many loaves of bread did the bakery have left? |
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Respond as succinctly as possible. Format the response as a completion of this table: |
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|step|subquestion|procedure|result| |
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|:---|:----------|:--------|:-----:|""" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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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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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:") |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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``` |
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***Generation: |
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|1|Calculate total sold|Add morning and afternoon sales|132| |
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|2|Subtract sold from total|200 - 132|68| |
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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?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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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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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:") |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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``` |
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***Generation: |
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To make a Spicy Margarita, you'll need to incorporate a chili or pepper element into your classic margarita recipe. Here’s a simple way to do it: |
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### Ingredients: |
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- 2 oz tequila (blanco or reposado) |
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- 1 oz fresh lime juice |
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- 1/2 oz triple sec (Cointreau or Grand Marnier) |
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- 1/2 oz agave syrup or simple syrup |
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- 1-2 slices of jalapeño (or more depending on how spicy you like it) |
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- Salt and/or chili powder for rimming the glass |
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- Ice |
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- Lime wheel for garnish |
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### Instructions: |
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1. **Muddle Jalapeño**: In a shaker, muddle the jalapeño slices slightly. This will release the oils and heat from the peppers. |
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2. **Add Remaining Ingredients**: Add the tequila, lime juice, triple sec, and agave syrup or simple syrup. |
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3. **Shake and Strain**: Fill the shaker with ice and shake vigorously until cold. Strain into a salt and/or chili powder rimmed glass filled with ice. |
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4. **Garnish and Serve**: Garnish with a lime wheel and enjoy. |
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If you prefer a smoother spiciness that doesn't overpower the drink, you could also consider making a jalapeño-infused tequila by leaving the jalapeño slices in the bottle of tequila for several hours to a couple of days, adjusting the time based on desired level of spiciness. Then use this infused tequila instead of regular tequila in the recipe above. |
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Another variation is to use a spicy syrup. To make this, combine equal parts water and sugar with a few sliced jalapeños in a saucepan. Bring to a boil, stirring occasionally to dissolve the sugar. Reduce heat and simmer for about 5 minutes. Let cool, strain out the jalapeños, then store in a sealed container in the refrigerator until ready to use. Use this spicy syrup instead of regular syrup in the recipe. |
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As always, adjust the quantity of jalapeño or the type of chili used to suit your taste. Enjoy responsibly! |
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``` |
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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__CalmeRys-78B-Orpo-v0.1) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |50.78| |
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|IFEval (0-Shot) |81.63| |
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|BBH (3-Shot) |61.92| |
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|MATH Lvl 5 (4-Shot)|37.92| |
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|GPQA (0-shot) |20.02| |
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|MuSR (0-shot) |36.37| |
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|MMLU-PRO (5-shot) |66.80| |
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