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Model: Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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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: NeuralLLaMa-3-8b-ORPO-v0.3
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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: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 69.54
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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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: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 84.9
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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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 (5-Shot)
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type: cais/mmlu
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config: all
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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: 68.39
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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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: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 60.82
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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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: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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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: 79.4
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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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: GSM8k (5-shot)
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type: gsm8k
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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: 72.93
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3
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name: Open LLM Leaderboard
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base_model:
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- meta-llama/Meta-Llama-3.1-8B-Instruct
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---
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# NeuralLLaMa-3-8b-ORPO-v0.3
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```python
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!pip install -qU transformers accelerate bitsandbytes
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
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import torch
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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MODEL_NAME = 'Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.3'
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config)
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prompt_system = "Sos un modelo de lenguaje de avanzada que habla español de manera fluida, clara y precisa.\
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Te llamas Roberto el Robot y sos un aspirante a artista post moderno"
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prompt = "Creame una obra de arte que represente tu imagen de como te ves vos roberto como un LLm de avanzada, con arte ascii, mezcla diagramas, ingenieria y dejate llevar"
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chat = [
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{"role": "system", "content": f"{prompt_system}"},
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{"role": "user", "content": f"{prompt}"},
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]
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chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(chat, return_tensors="pt").to('cuda')
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streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=1024, do_sample=True, temperature=0.3, repetition_penalty=1.2, top_p=0.9,)
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralLLaMa-3-8b-ORPO-v0.3)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |72.66|
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|AI2 Reasoning Challenge (25-Shot)|69.54|
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|HellaSwag (10-Shot) |84.90|
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|MMLU (5-Shot) |68.39|
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|TruthfulQA (0-shot) |60.82|
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|Winogrande (5-shot) |79.40|
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|GSM8k (5-shot) |72.93|
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