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Model: ahxt/llama2_xs_460M_experimental Source: Original Platform
2026-07-20 22:36:28 +08:00
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+---
+language: 
+  - en
+tags:
+- llama2
+- llama-2
+- llama
+- llama2 architecture
+datasets:
+- Redpajama
+metrics:
+- MMLU
+---
+
+# 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         |