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[docs] convert supported configs to table (#20858) 


Signed-off-by: default avatarreidliu41 <reid201711@gmail.com>
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docs/getting_started/installation/intel_gaudi.md
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......@@ -133,36 +133,20 @@ docker run \
The following configurations have been validated to function with
Gaudi2 devices. Configurations that are not listed may or may not work.
- [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
on single HPU, or with tensor parallelism on 2x and 8x HPU, BF16
datatype with random or greedy sampling
- [meta-llama/Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
- [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)
with tensor parallelism on 8x HPU, BF16 datatype with random or greedy sampling
| Model | TP Size| dtype | Sampling |
|-------|--------|--------|----------|
| [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) | 1, 2, 8 | BF16 | Random / Greedy |
| [meta-llama/Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b) | 8 | BF16 | Random / Greedy |
| [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) | 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B) | 8 | BF16 | Random / Greedy |
| [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) | 8 | BF16 | Random / Greedy |
## Performance tuning
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