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@@ -116,7 +116,140 @@ The AutoAWQ script has been adapted from [AutoAWQ/examples/generate.py](https://
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  ### 🤗 Text Generation Inference (TGI)
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- Coming soon!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Quantization Reproduction
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  ### 🤗 Text Generation Inference (TGI)
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+ To run the `text-generation-launcher` with Llama 3.1 405B Instruct AWQ in INT4 with Marlin kernels for optimized inference speed, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)) and the `huggingface_hub` Python package as you need to login to the Hugging Face Hub.
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+
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+ ```bash
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+ pip install -q --upgrade huggingface_hub
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+ huggingface-cli login
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+ ```
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+
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+ Then you just need to run the TGI v2.2.0 (or higher) Docker container as follows:
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+
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+ ```bash
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+ docker run --gpus all --shm-size 1g -ti -p 8080:80 \
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+ -v hf_cache:/data \
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+ -e MODEL_ID=hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 \
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+ -e NUM_SHARD=8 \
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+ -e QUANTIZE=awq \
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+ -e HF_TOKEN=$(cat ~/.cache/huggingface/token) \
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+ -e MAX_INPUT_LENGTH=4000 \
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+ -e MAX_TOTAL_TOKENS=4096 \
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+ ghcr.io/huggingface/text-generation-inference:2.2.0
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+ ```
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+
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+ > [!NOTE]
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+ > TGI will expose different endpoints, to see all the endpoints available check [TGI OpenAPI Specification](https://huggingface.github.io/text-generation-inference/#/).
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+
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+ To send request to the deployed TGI endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`:
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+
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+ ```bash
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+ curl 0.0.0.0:8080/v1/chat/completions \
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+ -X POST \
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+ -H 'Content-Type: application/json' \
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+ -d '{
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+ "model": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
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+ "messages": [
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+ {
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+ "role": "system",
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+ "content": "You are a helpful assistant."
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+ },
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+ {
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+ "role": "user",
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+ "content": "What is Deep Learning?"
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+ }
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+ ],
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+ "max_tokens": 128
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+ }'
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+ ```
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+
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+ Or programatically via the `huggingface_hub` Python client as follows:
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+
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+ ```python
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+ import os
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+ from huggingface_hub import InferenceClient
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+
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+ client = InferenceClient(base_url="http://0.0.0.0:8080", api_key=os.getenv("HF_TOKEN", "-"))
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+
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+ chat_completion = client.chat.completions.create(
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+ model="hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
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+ messages=[
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+ {"role": "system", "content": "You are a helpful assistant."},
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+ {"role": "user", "content": "What is Deep Learning?"},
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+ ],
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+ max_tokens=128,
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+ )
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+ ```
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+
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+ Alternatively, the OpenAI Python client can also be used (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows:
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+
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+ ```python
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+ import os
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+ from openai import OpenAI
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+
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+ client = OpenAI(base_url="http://0.0.0.0:8080/v1", api_key=os.getenv("OPENAI_API_KEY", "-"))
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+
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+ chat_completion = client.chat.completions.create(
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+ model="tgi",
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+ messages=[
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+ {"role": "system", "content": "You are a helpful assistant."},
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+ {"role": "user", "content": "What is Deep Learning?"},
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+ ],
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+ max_tokens=128,
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+ )
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+ ```
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+
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+ ### vLLM
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+
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+ To run vLLM with Llama 3.1 405B Instruct AWQ in INT4, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)) and run the latest vLLM Docker container as follows:
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+
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+ ```bash
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+ docker run --runtime nvidia --gpus all --ipc=host -p 8000:8000 \
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+ -v hf_cache:/root/.cache/huggingface \
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+ vllm/vllm-openai:latest \
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+ --model hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 \
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+ --tensor-parallel-size 8 \
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+ --max-model-len 4096
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+ ```
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+
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+ To send request to the deployed vLLM endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`:
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+
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+ ```bash
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+ curl 0.0.0.0:8000/v1/chat/completions \
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+ -X POST \
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+ -H 'Content-Type: application/json' \
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+ -d '{
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+ "model": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
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+ "messages": [
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+ {
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+ "role": "system",
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+ "content": "You are a helpful assistant."
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+ },
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+ {
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+ "role": "user",
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+ "content": "What is Deep Learning?"
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+ }
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+ ],
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+ "max_tokens": 128
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+ }'
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+ ```
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+
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+ Or programatically via the `openai` Python client (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows:
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+
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+ ```python
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+ import os
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+ from openai import OpenAI
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+
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+ client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key=os.getenv("VLLM_API_KEY", "-"))
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+
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+ chat_completion = client.chat.completions.create(
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+ model="hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
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+ messages=[
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+ {"role": "system", "content": "You are a helpful assistant."},
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+ {"role": "user", "content": "What is Deep Learning?"},
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+ ],
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+ max_tokens=128,
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+ )
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+ ```
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  ## Quantization Reproduction
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