Instructions to use Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit") model = AutoModelForCausalLM.from_pretrained("Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit
- SGLang
How to use Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit 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 "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit" \ --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": "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit", "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 "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit" \ --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": "Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit with Docker Model Runner:
docker model run hf.co/Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit
Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit
This model was converted to GGUF format from meta-llama/Llama-3.3-70B-Instruct using llama.cpp
This model was converted to GGUF format from unsloth/Llama-3.3-70B-Instruct-bnb-4bit using llama.cpp
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux) from []
brew install llama.cpp or !git clone https://github.com/ggerganov/llama.cpp.git
Invoke the llama.cpp server or the CLI.
CLI:
! /content/llama.cpp/llama-cli -m ./Llama-3.3-70B-4bit -n 90 --repeat_penalty 1.0 --color -i -r "User:" -f /content/llama.cpp/prompts/chat-with-bob.txt
or
llama-cli --hf-repo Sri-Vigneshwar-DJ/meta-llama/Llama-3.3-70B-4bit --hf-file FP8.gguf -p "Create Meta Ads Templates"
Server:
llama-server --hf-repo Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit --hf-file FP8.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag or ''!make GGML_OPENBLAS=1' along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
or
!make GGML_OPENBLAS=1
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit --hf-file FP8.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit --hf-file sFP8.gguf -c 2048
Step 4: On Ollama
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Model tree for Sri-Vigneshwar-DJ/Llama-3.3-70B-4bit
Base model
meta-llama/Llama-3.1-70B