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README.md

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+---
+base_model: Maykeye/TinyLLama-v0
+inference: false
+license: apache-2.0
+model_creator: Maykeye
+model_name: TinyLLama-v0
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# Maykeye/TinyLLama-v0-GGUF
+
+Quantized GGUF model files for [TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0) from [Maykeye](https://huggingface.co/Maykeye)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [tinyllama-v0.fp16.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.fp16.gguf) | fp16 | 11.08 MB  |
+| [tinyllama-v0.q2_k.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q2_k.gguf) | q2_k | 5.47 MB  |
+| [tinyllama-v0.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q3_k_m.gguf) | q3_k_m | 5.63 MB  |
+| [tinyllama-v0.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q4_k_m.gguf) | q4_k_m | 5.79 MB  |
+| [tinyllama-v0.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q5_k_m.gguf) | q5_k_m | 5.95 MB  |
+| [tinyllama-v0.q6_k.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q6_k.gguf) | q6_k | 6.72 MB  |
+| [tinyllama-v0.q8_0.gguf](https://huggingface.co/afrideva/TinyLLama-v0-GGUF/resolve/main/tinyllama-v0.q8_0.gguf) | q8_0 | 6.75 MB  |
+
+
+
+## Original Model Card:
+This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.
+
+* Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading 
+TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running
+the cells. Validation content is not used by the script so you put anythin in 
+
+* Backup directory has a script do_backup that I used to copy weights from remote machine to local.
+Weight are generated too quickly, so by the time script copied weihgt N+1 
+
+* This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use
+any sliding window to train story not from the start
+
+* Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
+
+* I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69).
+I had no troubles on the cloud machine with preninstalled libraries.
+
+* Demo script is demo.py
+
+* Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`:
+After training I decided that it's not necessary to beat validation into chunks
+
+* Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks
+so if random shuffle asks for a story, it may use cache or load chunk. 
+Training dataset is too small, so in next versions I will get rid of it.
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer