Instructions to use meta-llama/Meta-Llama-3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Meta-Llama-3-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meta-llama/Meta-Llama-3-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meta-llama/Meta-Llama-3-8B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meta-llama/Meta-Llama-3-8B-Instruct
- SGLang
How to use meta-llama/Meta-Llama-3-8B-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "meta-llama/Meta-Llama-3-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "meta-llama/Meta-Llama-3-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use meta-llama/Meta-Llama-3-8B-Instruct with Docker Model Runner:
docker model run hf.co/meta-llama/Meta-Llama-3-8B-Instruct
How to stop Llama from generating follow-up questions and excessive conversation with langchain
why does Llama generate random questions and answer them by itself, it's so annoying and overcomplicates stuff. I'm using Langchain framework and my prompt template code looks something like this:
system_prompt = (
"Use the given context to answer the question. "
"If you don't know the answer, say you don't know. "
"Use three sentence maximum and keep the answer concise. "
"Context: {context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
def get_response_llm_pinecone(llm, vectorstore, query):
Create the question-answer chain
question_answer_chain = create_stuff_documents_chain(llm, prompt)
# Create the retrieval chain
chain = create_retrieval_chain(vectorstore.as_retriever(), question_answer_chain)
# Invoke the retrieval chain with the query
response = chain.invoke({"input": query})
print(response["input"])
print(response["answer"])
This is a common problem with these langchain and llama_index libraries. They keep on generating responses. I would recommend to work directly with the transformers library as shown in the examples on this page https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
