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---
base_model: ahxt/llama2_xs_460M_experimental
datasets:
- Redpajama
inference: false
language:
- en
metrics:
- MMLU
model_creator: ahxt
model_name: llama2_xs_460M_experimental
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- llama2
- llama-2
- llama
- llama2 architecture
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# ahxt/llama2_xs_460M_experimental-GGUF
Quantized GGUF model files for [llama2_xs_460M_experimental](https://huggingface.co/ahxt/llama2_xs_460M_experimental) from [ahxt](https://huggingface.co/ahxt)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama2_xs_460m_experimental.q2_k.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q2_k.gguf) | q2_k | 212.56 MB |
| [llama2_xs_460m_experimental.q3_k_m.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q3_k_m.gguf) | q3_k_m | 238.87 MB |
| [llama2_xs_460m_experimental.q4_k_m.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q4_k_m.gguf) | q4_k_m | 288.51 MB |
| [llama2_xs_460m_experimental.q5_k_m.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q5_k_m.gguf) | q5_k_m | 333.29 MB |
| [llama2_xs_460m_experimental.q6_k.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q6_k.gguf) | q6_k | 380.87 MB |
| [llama2_xs_460m_experimental.q8_0.gguf](https://huggingface.co/afrideva/llama2_xs_460M_experimental-GGUF/resolve/main/llama2_xs_460m_experimental.q8_0.gguf) | q8_0 | 492.67 MB |
## Original Model Card:
# LLaMa Lite: Reduced-Scale, Experimental Versions of LLaMA and LLaMa 2
In this series of repos, we present an open-source reproduction of Meta AI's [LLaMA](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) and [LLaMa 2](https://ai.meta.com/llama/) large language models. However, with significantly reduced model sizes, the experimental version of [llama1_s](https://huggingface.co/ahxt/llama1_s_1.8B_experimental) has 1.8B parameters, and the experimental version of [llama2_xs](https://huggingface.co/ahxt/llama2_xs_460M_experimental) has 460M parameters. ('s' stands for small, while 'xs' denotes extra small).
## 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.
### 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/llama2_xs_460M_experimental'
model_path = 'ahxt/llama1_s_1.8B_experimental'
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 the bald eagle.
```
## Evaluation
We evaluate our models on the MMLU task
markdown table
| 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 |
| llama2_xs_460M | 0.46B | 21.13 | 26.39 |
## Contact
This experimental version is developed by:
[Xiaotian Han](https://ahxt.github.io/) from Texas A&M University. And these experimental verisons are for research only.
# [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 | |