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Richard Erkhov
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README.md
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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openai-gsm8k_meta-llama-Llama-3.2-1B - GGUF
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- Model creator: https://huggingface.co/YWZBrandon/
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- Original model: https://huggingface.co/YWZBrandon/openai-gsm8k_meta-llama-Llama-3.2-1B/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q2_K.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q2_K.gguf) | Q2_K | 0.54GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_XS.gguf) | IQ3_XS | 0.58GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_S.gguf) | IQ3_S | 0.6GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_S.gguf) | Q3_K_S | 0.6GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.IQ3_M.gguf) | IQ3_M | 0.61GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K.gguf) | Q3_K | 0.64GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_M.gguf) | Q3_K_M | 0.64GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q3_K_L.gguf) | Q3_K_L | 0.68GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.IQ4_XS.gguf) | IQ4_XS | 0.7GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_0.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_0.gguf) | Q4_0 | 0.72GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.IQ4_NL.gguf) | IQ4_NL | 0.72GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K_S.gguf) | Q4_K_S | 0.72GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K.gguf) | Q4_K | 0.75GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_K_M.gguf) | Q4_K_M | 0.75GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_1.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q4_1.gguf) | Q4_1 | 0.77GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_0.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_0.gguf) | Q5_0 | 0.83GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K_S.gguf) | Q5_K_S | 0.83GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K.gguf) | Q5_K | 0.85GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_K_M.gguf) | Q5_K_M | 0.85GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_1.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q5_1.gguf) | Q5_1 | 0.89GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q6_K.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q6_K.gguf) | Q6_K | 0.95GB |
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| [openai-gsm8k_meta-llama-Llama-3.2-1B.Q8_0.gguf](https://huggingface.co/RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf/blob/main/openai-gsm8k_meta-llama-Llama-3.2-1B.Q8_0.gguf) | Q8_0 | 1.23GB |
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Original model description:
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---
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base_model: meta-llama/Llama-3.2-1B
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datasets: openai/gsm8k
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library_name: transformers
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model_name: openai-gsm8k_meta-llama-Llama-3.2-1B
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for openai-gsm8k_meta-llama-Llama-3.2-1B
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This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the [openai/gsm8k](https://huggingface.co/datasets/openai/gsm8k) dataset.
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="YWZBrandon/openai-gsm8k_meta-llama-Llama-3.2-1B", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yuweiz/ActionEditV1/runs/fz4agnju)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.12.2
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- Transformers: 4.46.3
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- Pytorch: 2.5.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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