Instructions to use Arc53/DocsGPT-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arc53/DocsGPT-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arc53/DocsGPT-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Arc53/DocsGPT-7B") model = AutoModelForCausalLM.from_pretrained("Arc53/DocsGPT-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Arc53/DocsGPT-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arc53/DocsGPT-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arc53/DocsGPT-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arc53/DocsGPT-7B
- SGLang
How to use Arc53/DocsGPT-7B 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 "Arc53/DocsGPT-7B" \ --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": "Arc53/DocsGPT-7B", "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 "Arc53/DocsGPT-7B" \ --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": "Arc53/DocsGPT-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arc53/DocsGPT-7B with Docker Model Runner:
docker model run hf.co/Arc53/DocsGPT-7B
serving the model locally with cpu
when using the model locally with my macBook pro with:
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.llms import HuggingFacePipeline
os.environ["HUGGINGFACEHUB_API_TOKEN"] = '***********************'
tokenizer = AutoTokenizer.from_pretrained("Arc53/DocsGPT-7B")
model = AutoModelForCausalLM.from_pretrained("Arc53/DocsGPT-7B",
trust_remote_code=True)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=1024
)
local_llm = HuggingFacePipeline(pipeline=pipe)
.....
got :
the model 'MPTForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM'.....
Entering new chain...
Input length of input_ids is 1636, butmax_lengthis set to 1024. This can lead to unexpected behavior. You should consider increasingmax_new_tokens.
do i have any way of using this model with a langchain's agent on a CPU computer ?