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Llama 3.1 70B

Model Overview

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

QPC Configurations

Precision SoCs / Tensor slicing NSP-Cores (per SoC) Full Batch Size Chunking Prompt Length Context Length (CL) Generated URL Download
MXFP6 8 16 8 128 8192 https://qualcom-qpc-models.s3-accelerate.amazonaws.com/SDK1.20.4/meta-llama/Llama-3.1-70B-Instruct/qpc_16cores_128pl_8192cl_8fbs_8devices_mxfp6_mxint8.tar.gz Download
MXFP6 4 16 1 128 8192 https://qualcom-qpc-models.s3-accelerate.amazonaws.com/SDK1.20.4/meta-llama/Llama-3.1-70B-Instruct/qpc_16cores_128pl_8192cl_1fbs_4devices_mxfp6_mxint8.tar.gz Download

Run This Model

# Download QPC
mkdir -p meta-llama/Llama-3.1-70B-Instruct
cd meta-llama/Llama-3.1-70B-Instruct
wget <Download URL>
tar xzvf <downloaded filename.tar.gz>

# Run QPC
python3 -m QEfficient.cloud.execute --model_name meta-llama/Llama-3.1-70B-Instruct --qpc_path <path/to/qpc> --prompt "# shortest path algorithm\n" --generation_len 128

API Endpoint

# Start REST endpoint with vLLM
VLLM_QAIC_MAX_CPU_THREADS=8 VLLM_QAIC_QPC_PATH=/path/to/qpc python3 -m vllm.entrypoints.openai.api_server \
  --host 0.0.0.0 \
  --port 8000 \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --max-model-len <Context Length> \
  --max-num-seq <Full Batch Size>  \
  --max-seq_len-to-capture <Chunking Prompt Length>  \
  --device qaic \
  --block-size 32