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Model: afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF Source: Original Platform
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.fp16.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q2_k.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q3_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q5_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q6_k.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
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datasets:
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- databricks/databricks-dolly-15k
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inference: false
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language:
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- en
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license: apache-2.0
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model_creator: habanoz
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model_name: TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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---
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# habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF
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Quantized GGUF model files for [TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1](https://huggingface.co/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1) from [habanoz](https://huggingface.co/habanoz)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.fp16.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.fp16.gguf) | fp16 | 2.20 GB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q2_k.gguf) | q2_k | 483.12 MB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q3_k_m.gguf) | q3_k_m | 550.82 MB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q4_k_m.gguf) | q4_k_m | 668.79 MB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q5_k_m.gguf) | q5_k_m | 783.02 MB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q6_k.gguf) | q6_k | 904.39 MB |
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| [tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1-GGUF/resolve/main/tinyllama-1.1b-2t-lr-2e-4-3ep-dolly-15k-instruct-v1.q8_0.gguf) | q8_0 | 1.17 GB |
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## Original Model Card:
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TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T finetuned using dolly dataset.
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Training took 1 hour on an 'ml.g5.xlarge' instance.
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```python
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hyperparameters ={
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'num_train_epochs': 3, # number of training epochs
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'per_device_train_batch_size': 6, # batch size for training
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'gradient_accumulation_steps': 2, # Number of updates steps to accumulate
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'gradient_checkpointing': True, # save memory but slower backward pass
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'bf16': True, # use bfloat16 precision
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'tf32': True, # use tf32 precision
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'learning_rate': 2e-4, # learning rate
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'max_grad_norm': 0.3, # Maximum norm (for gradient clipping)
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'warmup_ratio': 0.03, # warmup ratio
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"lr_scheduler_type":"constant", # learning rate scheduler
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'save_strategy': "epoch", # save strategy for checkpoints
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"logging_steps": 10, # log every x steps
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'merge_adapters': True, # wether to merge LoRA into the model (needs more memory)
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'use_flash_attn': True, # Whether to use Flash Attention
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}
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```
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