Instructions to use dotan1111/BetaInfer_Configuration1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dotan1111/BetaInfer_Configuration1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dotan1111/BetaInfer_Configuration1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dotan1111/BetaInfer_Configuration1")
model = AutoModelForCausalLM.from_pretrained("dotan1111/BetaInfer_Configuration1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dotan1111/BetaInfer_Configuration1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dotan1111/BetaInfer_Configuration1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dotan1111/BetaInfer_Configuration1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dotan1111/BetaInfer_Configuration1

SGLang

How to use dotan1111/BetaInfer_Configuration1 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 "dotan1111/BetaInfer_Configuration1" \
    --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": "dotan1111/BetaInfer_Configuration1",
		"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 "dotan1111/BetaInfer_Configuration1" \
        --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": "dotan1111/BetaInfer_Configuration1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dotan1111/BetaInfer_Configuration1 with Docker Model Runner:
```
docker model run hf.co/dotan1111/BetaInfer_Configuration1
```

dotan1111 commited on Aug 4

Commit

b227be8

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1 Parent(s): bc5a4b5

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@@ -21,6 +21,8 @@ Accurate inference of phylogenetic trees is fundamental to evolutionary biology,
 Illustration of predicting a phylogenetic tree using BetaInfer. Panel (a) shows the simulated “true” evolutionary dynamics, in which the ancestral sequence “AAMM” diverged along a phylogenetic tree evolving into four leaves: “ADAM”, “ADAW”, “AAM”, and “ATMMM”. Panel (b) illustrates the BetaReconstruct pipeline: (Ⅰ): unaligned protein sequences serve as input for our model; (Ⅱ): The unaligned protein sequences are concatenated into the input “sentence”, and the special token marking the start of the phylogenetic tree inference is added; (Ⅲ): Our trained model processes the input and generates the phylogenetic tree (the output); (Ⅳ): the generated phylogenetic tree. Panels (c) highlights the labeling of the taxa in the resulting tree, in which, the first protein sequence is labeled ℓ0, and the second is labeled ℓ1, and so forth.
 ## Public models
 **[IMPORTANT] Note on Generalization**: While we have verified the models' ability to generalize to out-of-distribution data (as detailed in the main text), performance is highest when inference is performed on data distributions similar to those used during training.

 Illustration of predicting a phylogenetic tree using BetaInfer. Panel (a) shows the simulated “true” evolutionary dynamics, in which the ancestral sequence “AAMM” diverged along a phylogenetic tree evolving into four leaves: “ADAM”, “ADAW”, “AAM”, and “ATMMM”. Panel (b) illustrates the BetaReconstruct pipeline: (Ⅰ): unaligned protein sequences serve as input for our model; (Ⅱ): The unaligned protein sequences are concatenated into the input “sentence”, and the special token marking the start of the phylogenetic tree inference is added; (Ⅲ): Our trained model processes the input and generates the phylogenetic tree (the output); (Ⅳ): the generated phylogenetic tree. Panels (c) highlights the labeling of the taxa in the resulting tree, in which, the first protein sequence is labeled ℓ0, and the second is labeled ℓ1, and so forth.
+## link: https://www.biorxiv.org/content/10.64898/2026.06.14.732140v1
 ## Public models
 **[IMPORTANT] Note on Generalization**: While we have verified the models' ability to generalize to out-of-distribution data (as detailed in the main text), performance is highest when inference is performed on data distributions similar to those used during training.