RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf
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Model: RichardErkhov/YWZBrandon_-_openai-gsm8k_meta-llama-Llama-3.2-1B-gguf
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Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
openai-gsm8k_meta-llama-Llama-3.2-1B - GGUF
- Model creator: https://huggingface.co/YWZBrandon/
- Original model: https://huggingface.co/YWZBrandon/openai-gsm8k_meta-llama-Llama-3.2-1B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
Original model description:
---
base_model: meta-llama/Llama-3.2-1B
datasets: openai/gsm8k
library_name: transformers
model_name: openai-gsm8k_meta-llama-Llama-3.2-1B
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for openai-gsm8k_meta-llama-Llama-3.2-1B
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.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
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?"
generator = pipeline("text-generation", model="YWZBrandon/openai-gsm8k_meta-llama-Llama-3.2-1B", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<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)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
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},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```

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