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README.md
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- trl
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- sft
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- generated_from_trainer
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base_model: NousResearch/Llama-2-7b-chat-hf
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datasets:
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- generator
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model-index:
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- name: sinhala-llama-2-7b-chat-hf
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# sinhala-llama-2-7b-chat-hf
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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- trl
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- sft
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- generated_from_trainer
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- text-generation-inference
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base_model: NousResearch/Llama-2-7b-chat-hf
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datasets:
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- generator
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- Thimira/sinhala-llama-2-data-format
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model-index:
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- name: sinhala-llama-2-7b-chat-hf
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results: []
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language:
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- si
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# sinhala-llama-2-7b-chat-hf
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the [Thimira/sinhala-llama-2-data-format](https://huggingface.co/datasets/Thimira/sinhala-llama-2-data-format) dataset.
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## Model description
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This is a model for Sinhala language text generation which is fine-tuned from the base llama-2-7b-chat-hf model.
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Currently the capabilities of themodel are extremely limited, and requires further data and fine-tuning to be useful. Feel free to experiment with the model and provide feedback.
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### Usage example
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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tokenizer = AutoTokenizer.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf")
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model = AutoModelForCausalLM.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf")
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pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
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prompt = "ඔබට සිංහල භාෂාව තේරුම් ගත හැකිද?"
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result = pipe(f"<s>[INST] {prompt} [/INST]")
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print(result[0]['generated_text'])
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```
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## Intended uses & limitations
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The Sinhala-LLaMA models are intended for assistant-like chat in the Sinhala language.
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To get the expected features and performance from these models the LLaMA 2 prompt format needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and breaklines in between.
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## Training and evaluation data
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