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Llama: change casing from LLaMa to LLaMA

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Akemi Izuko 2024-01-01 13:04:10 -07:00
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14
src/content/llama/a-history-of-llamas.md
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@ -34,8 +34,8 @@ Here's a brief timeline:
1. **March 2022**: InstructGPT paper is pre-print.
2. **November 2022**: ChatGPT is released.
3. **March 2023**: LLaMa (open source) and GPT4 is released.
4. **July 2023**: LLaMa 2 is released, alongside GGUF quantization.
3. **March 2023**: LLaMA (open source) and GPT4 is released.
4. **July 2023**: LLaMA 2 is released, alongside GGUF quantization.
5. **August 2023**: AWQ quantization paper.
6. **September 2023**: Mistral 7B is released.
7. **December 2023**: Mixtral 8x7B becomes the first MoE local llama.
@ -92,8 +92,8 @@ Nothing open source was even remotely close to GPT4 at this point.
#### Mid 2023
We finally see the local llama movement really take off around August 2023. Meta
released [LLaMa2](https://ai.meta.com/blog/llama-2/), which has decent
performance even on its 7B version. One key contribution of LLaMa2 was the GGUF
released [LLaMA2](https://ai.meta.com/blog/llama-2/), which has decent
performance even on its 7B version. One key contribution of LLaMA2 was the GGUF
quantization format. This format allows a model to be run on a mix of RAM and
VRAM, which meant many home computers could now run 4-bit quantized 7B models!
Previously, most enthusiasts would have to rent cloud GPUs to run their "local"
@ -101,9 +101,9 @@ llamas. Quantizing into GGUF is a very expensive process, so
[TheBloke](https://huggingface.co/TheBloke) on Huggingface emerges the defacto
source for [pre-quantized llamas](../quantization).
Based on LLaMa, the open source
Based on LLaMA, the open source
[llama.cpp](https://github.com/ggerganov/llama.cpp) becomes the leader of local
llama inference backends. Its support extends far beyond only running LLaMa2,
llama inference backends. Its support extends far beyond only running LLaMA2,
it's the first major backend to support running GGUF quantizations!
In addition, the [Activation-aware Weight
@ -114,7 +114,7 @@ quantized models. This is especially true for very heavily quantized models like
community at this point. AWQ lacks support anywhere at this time.
In late September 2023, out of nowhere came a French startup with a 7B model
that made leaps on top of LLaMa2 7B.
that made leaps on top of LLaMA2 7B.
[Mistral](https://mistral.ai/news/announcing-mistral-7b/) remains the best local
llama until mid-December 2023. Huge work in improving model tuning, particularly
character creation and code-assistant models, is done on top of Mistral 7B.

2
src/content/llama/localllama_links.md
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@ -13,7 +13,7 @@ heroImage: '/images/llama/llama-cool.avif'
locally-hosted (typically open source) llama, in contrast to commercially hosted
ones.
# Local LLaMa Quickstart
# Local LLaMA Quickstart
I've recently become aware of the open source LLM (local llama) movement. Unlike
traditional open source, the speed at which this field is moving is