Instructions to use Undi95/dbrx-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Undi95/dbrx-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Undi95/dbrx-base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Undi95/dbrx-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Undi95/dbrx-base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Undi95/dbrx-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Undi95/dbrx-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Undi95/dbrx-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Undi95/dbrx-base
- SGLang
How to use Undi95/dbrx-base 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 "Undi95/dbrx-base" \ --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": "Undi95/dbrx-base", "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 "Undi95/dbrx-base" \ --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": "Undi95/dbrx-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Undi95/dbrx-base with Docker Model Runner:
docker model run hf.co/Undi95/dbrx-base
Update README.md
#6
by Walmart-the-bag - opened
README.md
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@@ -86,8 +86,8 @@ export HF_HUB_ENABLE_HF_TRANSFER=1
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained("
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input_text = "Databricks was founded in "
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input_ids = tokenizer(input_text, return_tensors="pt")
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained("
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input_text = "Databricks was founded in "
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("Undi95/dbrx-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("Undi95/dbrx-base", device_map="cpu", torch_dtype=torch.bfloat16, trust_remote_code=True)
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input_text = "Databricks was founded in "
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input_ids = tokenizer(input_text, return_tensors="pt")
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("Undi95/dbrx-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("Undi95/dbrx-base", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
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input_text = "Databricks was founded in "
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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