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Update text-gen README.md to add auto-gptq fork install steps #1442

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regisss merged 2 commits into from
Oct 20, 2024
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Update text-gen README.md to add auto-gptq fork install steps #1442

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f48a84d
Update text-gen README.md to add auto-gptq fork install steps
hsubramony Oct 18, 2024
cf2b58a
update text-gen readme for tp strategy
hsubramony Oct 18, 2024
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6 changes: 5 additions & 1 deletion examples/text-generation/README.md
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Expand Up @@ -282,7 +282,7 @@ You will also need to add `--torch_compile` and `--parallel_strategy="tp"` in yo
Here is an example:
```bash
PT_ENABLE_INT64_SUPPORT=1 PT_HPU_LAZY_MODE=0 python ../gaudi_spawn.py --world_size 8 run_generation.py \
--model_name_or_path meta-llama/Llama-2-70b-hf \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--trim_logits \
--use_kv_cache \
--attn_softmax_bf16 \
Expand Down Expand Up @@ -593,6 +593,10 @@ For more details see [documentation](https://docs.habana.ai/en/latest/PyTorch/Mo
Llama2-7b in UINT4 weight only quantization is enabled using [AutoGPTQ Fork](https://github.com/HabanaAI/AutoGPTQ), which provides quantization capabilities in PyTorch.
Currently, the support is for UINT4 inference of pre-quantized models only.

```bash
BUILD_CUDA_EXT=0 python -m pip install -vvv --no-build-isolation git+https://github.com/HabanaAI/AutoGPTQ.git
```

You can run a *UINT4 weight quantized* model using AutoGPTQ by setting the following environment variables:
`SRAM_SLICER_SHARED_MME_INPUT_EXPANSION_ENABLED=false ENABLE_EXPERIMENTAL_FLAGS=true` before running the command,
and by adding the argument `--load_quantized_model_with_autogptq`.
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