Text Generation
Transformers
Safetensors
English
RefinedWebModel
custom_code
text-generation-inference
Instructions to use Arc53/docsgpt-7b-falcon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arc53/docsgpt-7b-falcon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arc53/docsgpt-7b-falcon", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Arc53/docsgpt-7b-falcon", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Arc53/docsgpt-7b-falcon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arc53/docsgpt-7b-falcon" # 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-falcon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arc53/docsgpt-7b-falcon
- SGLang
How to use Arc53/docsgpt-7b-falcon 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-falcon" \ --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-falcon", "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-falcon" \ --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-falcon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arc53/docsgpt-7b-falcon with Docker Model Runner:
docker model run hf.co/Arc53/docsgpt-7b-falcon
Commit History
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Update README.md
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readme upd
This model is fine tuned on top of falcon-7b-instruct DocsGPT is optimized for Documentation: Specifically fine-tuned for providing answers that are based on documentation provided in context, making it particularly useful for developers and technical support teams. We used 50k high quality examples to finetune it over 2 days on A10G GPU. We used lora fine tuning process. Its an apache-2.0 license so you can use it for commercial purposes too. # How to run it ``` from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "Arc53/docsgpt-7b-falcon" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelev…fff33c0 -
Update README.md
This model is fine tuned on top of falcon-7b-instruct DocsGPT is optimized for Documentation: Specifically fine-tuned for providing answers that are based on documentation provided in context, making it particularly useful for developers and technical support teams. We used 50k high quality examples to finetune it over 2 days on A10G GPU. We used lora fine tuning process. Its an apache-2.0 license so you can use it for commercial purposes too. # How to run it ``` from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "Arc53/docsgpt-7b-falcon" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelev…c11bf48 -
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