Instructions to use goendalf666/salesGPT_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use goendalf666/salesGPT_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="goendalf666/salesGPT_v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("goendalf666/salesGPT_v1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use goendalf666/salesGPT_v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "goendalf666/salesGPT_v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "goendalf666/salesGPT_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/goendalf666/salesGPT_v1
- SGLang
How to use goendalf666/salesGPT_v1 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 "goendalf666/salesGPT_v1" \ --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": "goendalf666/salesGPT_v1", "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 "goendalf666/salesGPT_v1" \ --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": "goendalf666/salesGPT_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use goendalf666/salesGPT_v1 with Docker Model Runner:
docker model run hf.co/goendalf666/salesGPT_v1
salesGPT_v1
salesGPT_v1_1
Extension of salesGPT_v1: https://huggingface.co/goendalf666/salesGPT_v1 In this version different LoRa configurations are tested. The backbone of this model is phi1_5: https://huggingface.co/microsoft/phi-1_5
References
The main model is salesGPT_v2: https://huggingface.co/goendalf666/salesGPT_v2 To see the code for the generation, inference and dataset generation see the github repo: https://github.com/tom813/salesGPT_foundation
The datasets that are used to fine tune this model are: https://huggingface.co/datasets/goendalf666/sales-conversations-instruction-base https://huggingface.co/datasets/goendalf666/sales-textbook_for_convincing_and_selling https://huggingface.co/datasets/goendalf666/sales-conversations-2
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 80000
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0.dev20230829+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for goendalf666/salesGPT_v1
Base model
microsoft/phi-1_5