Text Generation
Transformers
Safetensors
param1MoE
quantization
bitsandbytes
4-bit precision
MoE
bharatgenai
custom-quantization
custom_code
Instructions to use arunvenkat17/Param-1-7B-GodMode-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arunvenkat17/Param-1-7B-GodMode-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arunvenkat17/Param-1-7B-GodMode-4bit", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("arunvenkat17/Param-1-7B-GodMode-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arunvenkat17/Param-1-7B-GodMode-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arunvenkat17/Param-1-7B-GodMode-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arunvenkat17/Param-1-7B-GodMode-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arunvenkat17/Param-1-7B-GodMode-4bit
- SGLang
How to use arunvenkat17/Param-1-7B-GodMode-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 "arunvenkat17/Param-1-7B-GodMode-4bit" \ --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": "arunvenkat17/Param-1-7B-GodMode-4bit", "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 "arunvenkat17/Param-1-7B-GodMode-4bit" \ --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": "arunvenkat17/Param-1-7B-GodMode-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arunvenkat17/Param-1-7B-GodMode-4bit with Docker Model Runner:
docker model run hf.co/arunvenkat17/Param-1-7B-GodMode-4bit
- Xet hash:
- 2c80771a5770bf7030670f784a19b44959f6a0441e0acaa21660f8917e735a98
- Size of remote file:
- 15.2 MB
- SHA256:
- bbe10da9b146d68d399fc6743a7a9c4b7cb79512a97b42b0610981bad0b053e6
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