Instructions to use reshinthadith/BashGPTNeo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reshinthadith/BashGPTNeo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reshinthadith/BashGPTNeo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("reshinthadith/BashGPTNeo") model = AutoModelForCausalLM.from_pretrained("reshinthadith/BashGPTNeo") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use reshinthadith/BashGPTNeo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reshinthadith/BashGPTNeo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reshinthadith/BashGPTNeo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reshinthadith/BashGPTNeo
- SGLang
How to use reshinthadith/BashGPTNeo 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 "reshinthadith/BashGPTNeo" \ --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": "reshinthadith/BashGPTNeo", "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 "reshinthadith/BashGPTNeo" \ --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": "reshinthadith/BashGPTNeo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reshinthadith/BashGPTNeo with Docker Model Runner:
docker model run hf.co/reshinthadith/BashGPTNeo
YAML Metadata Error:"language[0]" must only contain lowercase characters
YAML Metadata Error:"language[0]" with value "English" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
YAML Metadata Error:"language[1]" must only contain lowercase characters
YAML Metadata Error:"language[1]" with value "Bash" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
BashGPT-Neo
What is it ?
BashGPT-Neo is a Neural Program Synthesis Model for Bash Commands and Shell Scripts. Trained on the data provided by NLC2CMD. It is fine-tuned version of GPTNeo-125M by EleutherAI.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("reshinthadith/BashGPTNeo")
model = AutoModelForCausalLM.from_pretrained("reshinthadith/BashGPTNeo")
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