Instructions to use DataPilot/ArrowSmart_1.7b_instant_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataPilot/ArrowSmart_1.7b_instant_sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DataPilot/ArrowSmart_1.7b_instant_sft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft") model = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DataPilot/ArrowSmart_1.7b_instant_sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DataPilot/ArrowSmart_1.7b_instant_sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DataPilot/ArrowSmart_1.7b_instant_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DataPilot/ArrowSmart_1.7b_instant_sft
- SGLang
How to use DataPilot/ArrowSmart_1.7b_instant_sft 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 "DataPilot/ArrowSmart_1.7b_instant_sft" \ --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": "DataPilot/ArrowSmart_1.7b_instant_sft", "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 "DataPilot/ArrowSmart_1.7b_instant_sft" \ --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": "DataPilot/ArrowSmart_1.7b_instant_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DataPilot/ArrowSmart_1.7b_instant_sft with Docker Model Runner:
docker model run hf.co/DataPilot/ArrowSmart_1.7b_instant_sft
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DataPilotVerified signature「LOCAL AI HACKATHON」における、チームDataPilot初めての成果品です。Line社が開発した「japanese-large-lm-1.7b-instruction-sft」をウィキブックの内容をもとに地理、化学の分野でファインチューニングを行いました。 how to use ``` import torch from transformers import AutoModelForSequenceClassification from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline #transformerとtorchがインストールされていることを前提とします。 model = AutoModelForCausalLM.from_pretrainedmodel = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft") tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft") generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0) torch.cuda.empty_cache() input_text = """有機物とは""" text = generator( f"ユーザー: {input_text} システム: ", max_length = 100, do_sample = True, temperature = 0.7, top_p = 0.9, top_k = 0, repetition_penalty = 1.1, num_beams = 1, pad_token_id = tokenizer.pad_token_id, num_return_sequences = 1, ) print(text) ``` トークン化: ユニグラム言…3087b57 -
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DataPilotVerified signature「LOCAL AI HACKATHON」における、チームDataPilot初めての成果品です。Line社が開発した「japanese-large-lm-1.7b-instruction-sft」をウィキブックの内容をもとに地理、化学の分野でファインチューニングを行いました。 how to use ``` import torch from transformers import AutoModelForSequenceClassification from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline #transformerとtorchがインストールされていることを前提とします。 model = AutoModelForCausalLM.from_pretrainedmodel = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft") tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmart_1.7b_instant_sft") generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0) torch.cuda.empty_cache() input_text = """有機物とは""" text = generator( f"ユーザー: {input_text} システム: ", max_length = 100, do_sample = True, temperature = 0.7, top_p = 0.9, top_k = 0, repetition_penalty = 1.1, num_beams = 1, pad_token_id = tokenizer.pad_token_id, num_return_sequences = 1, ) print(text) ``` トークン化: ユニグラム言…643770f -
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