231 lines
6.0 KiB
Markdown
231 lines
6.0 KiB
Markdown
---
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language:
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- en
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license: other
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library_name: transformers
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tags:
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- axolotl
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- finetune
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- facebook
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- meta
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- pytorch
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- llama
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- llama-3
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base_model: MaziyarPanahi/Llama-3-8B-Instruct-v0.8
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model_name: Llama-3-8B-Instruct-v0.9
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pipeline_tag: text-generation
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license_name: llama3
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license_link: LICENSE
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inference: false
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model_creator: MaziyarPanahi
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quantized_by: MaziyarPanahi
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model-index:
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- name: Llama-3-8B-Instruct-v0.9
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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: 72.35
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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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: 88.17
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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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: 68.1
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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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: 64.67
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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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: 79.95
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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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: 66.49
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Llama-3-8B-Instruct-v0.9
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name: Open LLM Leaderboard
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---
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<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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# Llama-3-8B-Instruct-v0.9
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This model was developed based on `MaziyarPanahi/Llama-3-8B-Instruct-v0.8` model.
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# ⚡ Quantized GGUF
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All GGUF models are available here: [MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF)
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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_MaziyarPanahi__Llama-3-8B-Instruct-v0.9)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |73.29|
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|AI2 Reasoning Challenge (25-Shot)|72.35|
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|HellaSwag (10-Shot) |88.17|
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|MMLU (5-Shot) |68.10|
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|TruthfulQA (0-shot) |64.67|
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|Winogrande (5-shot) |79.95|
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|GSM8k (5-shot) |66.49|
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`MaziyarPanahi/Llama-3-8B-Instruct-v0.9` is the 4th best-performing 8B model on the Open LLM Leaderboard. (03/06/2024).
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# Prompt Template
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This model uses `ChatML` prompt template:
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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````
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# How to use
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You can use this model by using `MaziyarPanahi/Llama-3-8B-Instruct-v0.9` as the model name in Hugging Face's
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transformers library.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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from transformers import pipeline
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import torch
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model_id = "MaziyarPanahi/Llama-3-8B-Instruct-v0.9"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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# attn_implementation="flash_attention_2"
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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trust_remote_code=True
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)
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streamer = TextStreamer(tokenizer)
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pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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model_kwargs={"torch_dtype": torch.bfloat16},
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streamer=streamer
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)
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# Then you can use the pipeline to generate text.
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = pipeline(
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prompt,
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max_new_tokens=512,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.95,
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)
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print(outputs[0]["generated_text"][len(prompt):])
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```
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