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Update README.md
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README.md
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@@ -13,7 +13,7 @@ pip install -e .
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## Description
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-
This model is intended to be used as an accelerator for [granite
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from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts
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a single token in the draft based on both a state vector and sampled token
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from the prior stage (the base model can be considered stage 0).
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docker pull $TGIS_IMAGE
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# optionally download granite-7b-
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docker run --rm \
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-v $HF_HUB_CACHE:/models \
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-e HF_HUB_CACHE=/models \
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-e TRANSFORMERS_CACHE=/models \
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$TGIS_IMAGE \
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text-generation-server download-weights \
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ibm-granite/granite-7b-
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--token $HF_HUB_TOKEN
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# optionally download the speculator model if the weights do not already exist
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@@ -64,7 +64,7 @@ docker run --rm \
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-e TRANSFORMERS_CACHE=/models \
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$TGIS_IMAGE \
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text-generation-server download-weights \
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ibm-granite/granite-7b-
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--token $HF_HUB_TOKEN
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# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
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-v $HF_HUB_CACHE:/models \
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-e HF_HUB_CACHE=/models \
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-e TRANSFORMERS_CACHE=/models \
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-e MODEL_NAME=ibm-granite/granite-7b-
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-e SPECULATOR_NAME=ibm-granite/granite-7b-
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-e FLASH_ATTENTION=true \
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-e PAGED_ATTENTION=true \
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-e DTYPE=float16 \
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#### start the server
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```bash
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model=ibm-granite/granite-7b-
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model
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```
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##### batch_size=1 (compile + cudagraphs)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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##### batch_size=1 (compile)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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##### batch_size=4 (compile)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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## Description
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This model is intended to be used as an accelerator for [granite-7b-instruct](https://huggingface.co/ibm-granite/granite-7b-instruct) and takes inspiration
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from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts
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a single token in the draft based on both a state vector and sampled token
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from the prior stage (the base model can be considered stage 0).
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docker pull $TGIS_IMAGE
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# optionally download granite-7b-instruct if the weights do not already exist
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docker run --rm \
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-v $HF_HUB_CACHE:/models \
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-e HF_HUB_CACHE=/models \
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-e TRANSFORMERS_CACHE=/models \
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$TGIS_IMAGE \
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text-generation-server download-weights \
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+
ibm-granite/granite-7b-instruct \
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--token $HF_HUB_TOKEN
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# optionally download the speculator model if the weights do not already exist
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-e TRANSFORMERS_CACHE=/models \
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$TGIS_IMAGE \
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text-generation-server download-weights \
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ibm-granite/granite-7b-instruct-accelerator \
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--token $HF_HUB_TOKEN
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# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
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-v $HF_HUB_CACHE:/models \
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-e HF_HUB_CACHE=/models \
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-e TRANSFORMERS_CACHE=/models \
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-e MODEL_NAME=ibm-granite/granite-7b-instruct \
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-e SPECULATOR_NAME=ibm-granite/granite-7b-instruct-accelerator \
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-e FLASH_ATTENTION=true \
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-e PAGED_ATTENTION=true \
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-e DTYPE=float16 \
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#### start the server
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```bash
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model=ibm-granite/granite-7b-instruct-accelerator
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model
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```
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##### batch_size=1 (compile + cudagraphs)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-instruct
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-instruct-accelerator \
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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##### batch_size=1 (compile)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-instruct
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-instruct-accelerator \
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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##### batch_size=4 (compile)
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```bash
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MODEL_PATH=/path/to/ibm-granite/granite-7b-instruct
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python fms-extras/scripts/paged_speculative_inference.py \
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--variant=7b.ibm_instruct_lab \
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--model_path=$MODEL_PATH \
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--model_source=hf \
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--tokenizer=$MODEL_PATH \
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--speculator_path=ibm-granite/granite-7b-instruct-accelerator \
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--speculator_source=hf \
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--speculator_variant=1_4b \
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--top_k_tokens_per_head=4,3,2,2,2 \
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