Instructions to use lambda/pythia-6.9b-deduped-synthetic-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lambda/pythia-6.9b-deduped-synthetic-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lambda/pythia-6.9b-deduped-synthetic-instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lambda/pythia-6.9b-deduped-synthetic-instruct") model = AutoModelForCausalLM.from_pretrained("lambda/pythia-6.9b-deduped-synthetic-instruct") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lambda/pythia-6.9b-deduped-synthetic-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lambda/pythia-6.9b-deduped-synthetic-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lambda/pythia-6.9b-deduped-synthetic-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lambda/pythia-6.9b-deduped-synthetic-instruct
- SGLang
How to use lambda/pythia-6.9b-deduped-synthetic-instruct 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 "lambda/pythia-6.9b-deduped-synthetic-instruct" \ --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": "lambda/pythia-6.9b-deduped-synthetic-instruct", "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 "lambda/pythia-6.9b-deduped-synthetic-instruct" \ --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": "lambda/pythia-6.9b-deduped-synthetic-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lambda/pythia-6.9b-deduped-synthetic-instruct with Docker Model Runner:
docker model run hf.co/lambda/pythia-6.9b-deduped-synthetic-instruct
Commit ·
eff7668
1
Parent(s): 4b409b2
Upload tokenizer
Browse files- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +10 -0
special_tokens_map.json
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tokenizer_config.json
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{
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"model_max_length": 1000000000000000019884624838656,
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"name_or_path": "/home/ubuntu/llm/outputs/ft-synthetic-instruct-gptj-pairwise-pythia6.9b-deepspeed/20230305-195729/checkpoint-500",
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"special_tokens_map_file": "/fsx/home-hailey/.cache/huggingface/hub/models--EleutherAI--gpt-neox-20b/snapshots/3523781c8df75f7741687a4284f6f70e1afa12f4/special_tokens_map.json",
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"tokenizer_class": "GPTNeoXTokenizer",
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"unk_token": "<|endoftext|>"
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}
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