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deepakmzn/tinyllama_2k_5e

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: results
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+ results: []
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+ library_name: peft
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # results
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+
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+ This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - quant_method: bitsandbytes
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+ - _load_in_8bit: False
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+ - _load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: nf4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+ - load_in_4bit: True
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+ - load_in_8bit: False
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.03
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.4.0
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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+ "bias": "none",
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+ "peft_type": "LORA",
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+ "target_modules": [
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+ "wqkv",
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+ "v_proj",
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+ "o_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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+ "bos_token": "<s>",
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+ "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
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+ "clean_up_tokenization_spaces": false,
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+ "padding_side": "right",
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+ "sp_model_kwargs": {},
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+ "tokenizer_class": "LlamaTokenizer",
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+ "unk_token": "<unk>",
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+ "use_default_system_prompt": false
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+ }
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