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README.md

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+---
+title: "LiteLlama-460M-1T Quantized in GGUF"
+tags:
+  - GGUF
+language: en
+---
+![Image description](https://i.postimg.cc/MGwhtFfF/tsune-fixed.png)
+
+# Tsunemoto GGUF's of LiteLlama-460M-1T
+
+This is a GGUF quantization of LiteLlama-460M-1T.
+
+## Original Repo Link:
+[Original Repository](https://huggingface.co/ahxt/LiteLlama-460M-1T)
+
+## Original Model Card:
+---
+
+# LiteLlama: Reduced-Scale Llama
+
+In this series of repos, we present an open-source reproduction of Meta AI's [LLaMa 2](https://ai.meta.com/llama/). However, with significantly reduced model sizes, [LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) has 460M parameters trained with 1T tokens.
+
+
+## Dataset and Tokenization
+We train our models on part of [RedPajama](https://www.together.xyz/blog/redpajama) dataset. We use the [GPT2Tokenizer](https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/gpt2#transformers.GPT2Tokenizer) to tokenize the text.
+
+## Training Details
+
+The model was trained with ~1T tokens (0.98T). num of tokens = steps*length*batch_size=499679*1024*192=98240888832≈0.98T.
+
+The training curve is at this [WandB project](https://wandb.ai/ahxt/llama2_xs_460M_training_loss/reports/reduced_train_loss-23-09-05-20-25-43---Vmlldzo1MzIwNDUx?accessToken=x2ch3n30jo77p1x8y7q9js4h4d8zpjtz1tzot4xxullyefixp4jwt7au2q37k2q6).
+
+### Using with HuggingFace Transformers
+The experimental checkpoints can be directly loaded by [Transformers](https://huggingface.co/transformers/) library. The following code snippet shows how to load the our experimental model and generate text with it. 
+
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_path = 'ahxt/LiteLlama-460M-1T'
+
+model = AutoModelForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.eval()
+
+prompt = 'Q: What is the largest bird?\nA:'
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+tokens = model.generate(input_ids, max_length=20)
+print( tokenizer.decode(tokens[0].tolist(), skip_special_tokens=True) )
+# Q: What is the largest bird?\nA: The largest bird is a black-headed gull.
+```
+
+## Evaluation
+
+### We evaluate our models on the MMLU task.
+
+| Models | #parameters |zero-shot |  5-shot |
+| --- | --- | --- | --- |
+| llama                       | 7B    | 28.46 | 35.05 |
+| openllama                   | 3B    | 24.90 | 26.71 |
+|TinyLlama-1.1B-step-50K-105b | 1.1B  | 19.00 | 26.53 |
+| LiteLlama-460M-1T           | 0.46B | 21.13 | 26.39 |
+
+
+### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ahxt__llama2_xs_460M_experimental)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 26.65   |
+| ARC (25-shot)         | 24.91          |
+| HellaSwag (10-shot)   | 38.47    |
+| MMLU (5-shot)         | 26.17         |
+| TruthfulQA (0-shot)   | 41.59   |
+| Winogrande (5-shot)   | 49.88   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 5.51         |
+
+
+
+
+## Contact
+This model is developed by [Xiaotian Han](https://ahxt.github.io/) from Texas A&M University and released under MIT License.
+
+
+
+
+
+
+
+
+
+

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