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