Instructions to use answerdotai/llama3-8b-instruct-CLA-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use answerdotai/llama3-8b-instruct-CLA-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="answerdotai/llama3-8b-instruct-CLA-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("answerdotai/llama3-8b-instruct-CLA-2") model = AutoModelForCausalLM.from_pretrained("answerdotai/llama3-8b-instruct-CLA-2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use answerdotai/llama3-8b-instruct-CLA-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "answerdotai/llama3-8b-instruct-CLA-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "answerdotai/llama3-8b-instruct-CLA-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/answerdotai/llama3-8b-instruct-CLA-2
- SGLang
How to use answerdotai/llama3-8b-instruct-CLA-2 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 "answerdotai/llama3-8b-instruct-CLA-2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "answerdotai/llama3-8b-instruct-CLA-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "answerdotai/llama3-8b-instruct-CLA-2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "answerdotai/llama3-8b-instruct-CLA-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use answerdotai/llama3-8b-instruct-CLA-2 with Docker Model Runner:
docker model run hf.co/answerdotai/llama3-8b-instruct-CLA-2
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Training Script: https://github.com/AnswerDotAI/fsdp_qlora/blob/3f7c583e985ff35e37a7b7497a7d4fedb77df695/experiments/cla/train.sh
This model shares KV activations every 2 layers. For example, layer 1 uses layer 0 kv activations, layer 3 uses layer 2 kv activations, etc..
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