Instructions to use aiplanet/effi-13b-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiplanet/effi-13b-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aiplanet/effi-13b-quant")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aiplanet/effi-13b-quant") model = AutoModelForCausalLM.from_pretrained("aiplanet/effi-13b-quant") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aiplanet/effi-13b-quant with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aiplanet/effi-13b-quant" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aiplanet/effi-13b-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aiplanet/effi-13b-quant
- SGLang
How to use aiplanet/effi-13b-quant 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 "aiplanet/effi-13b-quant" \ --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": "aiplanet/effi-13b-quant", "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 "aiplanet/effi-13b-quant" \ --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": "aiplanet/effi-13b-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aiplanet/effi-13b-quant with Docker Model Runner:
docker model run hf.co/aiplanet/effi-13b-quant
Chanukya Patnaik commited on
Commit ·
ff0b0ea
1
Parent(s): b13d56c
Update README.md
Browse files
README.md
CHANGED
|
@@ -33,7 +33,6 @@ Model Architecture Llama 2 is an auto-regressive language model that uses an opt
|
|
| 33 |
|
| 34 |
[kaist-ai/CoT-Collection](https://huggingface.co/datasets/kaist-ai/CoT-Collection)
|
| 35 |
|
| 36 |
-
The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style.
|
| 37 |
|
| 38 |
### Qunatization Configuration
|
| 39 |
|
|
|
|
| 33 |
|
| 34 |
[kaist-ai/CoT-Collection](https://huggingface.co/datasets/kaist-ai/CoT-Collection)
|
| 35 |
|
|
|
|
| 36 |
|
| 37 |
### Qunatization Configuration
|
| 38 |
|