Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
psmathur/orca_mini_v3_13b
garage-bAInd/Platypus2-13B
WizardLM/WizardMath-13B-V1.0
text-generation-inference
Instructions to use enegm/orca_mini_v3_13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enegm/orca_mini_v3_13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="enegm/orca_mini_v3_13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("enegm/orca_mini_v3_13b") model = AutoModelForCausalLM.from_pretrained("enegm/orca_mini_v3_13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use enegm/orca_mini_v3_13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "enegm/orca_mini_v3_13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "enegm/orca_mini_v3_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/enegm/orca_mini_v3_13b
- SGLang
How to use enegm/orca_mini_v3_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 "enegm/orca_mini_v3_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": "enegm/orca_mini_v3_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 "enegm/orca_mini_v3_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": "enegm/orca_mini_v3_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use enegm/orca_mini_v3_13b with Docker Model Runner:
docker model run hf.co/enegm/orca_mini_v3_13b
psmathur/orca_mini_v3_13b
psmathur/orca_mini_v3_13b is a merge of the following models using mergekit:
🧩 Configuration
models:
- model: psmathur/orca_mini_v3_13b
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: garage-bAInd/Platypus2-13B
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: WizardLM/WizardMath-13B-V1.0
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: TheBloke/Llama-2-13B-fp16
parameters:
normalize: true
int8_mask: true
dtype: float16```
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docker model run hf.co/enegm/orca_mini_v3_13b