Text Generation
Transformers
PyTorch
English
llama
llama-2
instruct
instruction
text-generation-inference
Instructions to use 42MARU/GenAI-llama-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 42MARU/GenAI-llama-2-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="42MARU/GenAI-llama-2-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("42MARU/GenAI-llama-2-13b") model = AutoModelForCausalLM.from_pretrained("42MARU/GenAI-llama-2-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use 42MARU/GenAI-llama-2-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "42MARU/GenAI-llama-2-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "42MARU/GenAI-llama-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/42MARU/GenAI-llama-2-13b
- SGLang
How to use 42MARU/GenAI-llama-2-13b 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 "42MARU/GenAI-llama-2-13b" \ --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": "42MARU/GenAI-llama-2-13b", "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 "42MARU/GenAI-llama-2-13b" \ --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": "42MARU/GenAI-llama-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 42MARU/GenAI-llama-2-13b with Docker Model Runner:
docker model run hf.co/42MARU/GenAI-llama-2-13b
GenAI-llama-2-13b
Model Details
- Developed by: 42MARU
- Backbone Model: LLaMA-2
- Library: transformers
Used Datasets
- Orca-style dataset
- Platypus
Prompt Template
### User:
{User}
### Assistant:
{Assistant}
Intruduce 42MARU
- At 42Maru we study QA (Question Answering) and are developing advanced search paradigms that help users spend less time searching by understanding natural language and intention thanks to AI and Deep Learning.
- About Us
- Contact Us
Contribute
License
USE_POLICY
Responsible Use Guide
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 56.03 |
| ARC (25-shot) | 63.14 |
| HellaSwag (10-shot) | 83.64 |
| MMLU (5-shot) | 59.91 |
| TruthfulQA (0-shot) | 56.21 |
| Winogrande (5-shot) | 76.72 |
| GSM8K (5-shot) | 9.4 |
| DROP (3-shot) | 43.23 |
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