Model save

README.md

@@ -4,16 +4,12 @@ tags:
 - trl
 - sft
 - generated_from_trainer
-- text-generation-inference
 base_model: NousResearch/Llama-2-7b-chat-hf
 datasets:
 - generator
-- Thimira/sinhala-llama-2-data-format
 model-index:
 - name: sinhala-llama-2-7b-chat-hf
   results: []
-language:
-- si
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -21,34 +17,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 # sinhala-llama-2-7b-chat-hf
 
-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.
+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 generator dataset.
 
 ## Model description
 
-This is a model for Sinhala language text generation which is fine-tuned from the base llama-2-7b-chat-hf model.
-
-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.
-
-### Usage example
-
-```
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf")
-
-pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-
-prompt = "ඔබට සිංහල භාෂාව තේරුම් ගත හැකිද?"
-result = pipe(f"<s>[INST] {prompt} [/INST]")
-print(result[0]['generated_text'])
-```
+More information needed
 
 ## Intended uses & limitations
 
-The Sinhala-LLaMA models are intended for assistant-like chat in the Sinhala language.
-
-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.
+More information needed
 
 ## Training and evaluation data
 
@@ -60,7 +37,7 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0002
-- train_batch_size: 2
+- train_batch_size: 4
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -75,7 +52,7 @@ The following hyperparameters were used during training:
 ### Framework versions
 
 - PEFT 0.10.0
-- Transformers 4.39.3
+- Transformers 4.40.1
 - Pytorch 2.1.0
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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