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--- |
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base_model: ahxt/llama2_xs_460M_experimental |
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datasets: |
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- Redpajama |
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inference: false |
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language: |
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- en |
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metrics: |
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- MMLU |
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model_creator: ahxt |
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model_name: llama2_xs_460M_experimental |
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pipeline_tag: text-generation |
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quantized_by: afrideva |
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tags: |
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- llama2 |
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- llama-2 |
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- llama |
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- llama2 architecture |
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- gguf |
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- ggml |
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- quantized |
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- q2_k |
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- q3_k_m |
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- q4_k_m |
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- q5_k_m |
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- q6_k |
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- q8_0 |
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--- |
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# ahxt/llama2_xs_460M_experimental-GGUF |
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Quantized GGUF model files for [llama2_xs_460M_experimental](https://huggingface.co/ahxt/llama2_xs_460M_experimental) from [ahxt](https://huggingface.co/ahxt) |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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## Original Model Card: |
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# LLaMa Lite: Reduced-Scale, Experimental Versions of LLaMA and LLaMa 2 |
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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). |
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## Dataset and Tokenization |
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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. |
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### Using with HuggingFace Transformers |
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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. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# model_path = 'ahxt/llama2_xs_460M_experimental' |
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model_path = 'ahxt/llama1_s_1.8B_experimental' |
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model = AutoModelForCausalLM.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model.eval() |
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prompt = 'Q: What is the largest bird?\nA:' |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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tokens = model.generate(input_ids, max_length=20) |
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print( tokenizer.decode(tokens[0].tolist(), skip_special_tokens=True) ) |
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# Q: What is the largest bird?\nA: The largest bird is the bald eagle. |
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``` |
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## Evaluation |
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We evaluate our models on the MMLU task |
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markdown table |
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| Models | #parameters |zero-shot | 5-shot | |
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| --- | --- | --- | --- | |
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| llama | 7B | 28.46 | 35.05 | |
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| openllama | 3B | 24.90 | 26.71 | |
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|TinyLlama-1.1B-step-50K-105b | 1.1B | 19.00 | 26.53 | |
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| llama2_xs_460M | 0.46B | 21.13 | 26.39 | |
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## Contact |
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This experimental version is developed by: |
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[Xiaotian Han](https://ahxt.github.io/) from Texas A&M University. And these experimental verisons are for research only. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ahxt__llama2_xs_460M_experimental) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 26.65 | |
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| ARC (25-shot) | 24.91 | |
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| HellaSwag (10-shot) | 38.47 | |
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| MMLU (5-shot) | 26.17 | |
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| TruthfulQA (0-shot) | 41.59 | |
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| Winogrande (5-shot) | 49.88 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 5.51 | |
