Instructions to use PhysiQuanty/Binary-Addition-LLM-POC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PhysiQuanty/Binary-Addition-LLM-POC with Transformers:

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

pipe = pipeline("text-generation", model="PhysiQuanty/Binary-Addition-LLM-POC", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PhysiQuanty/Binary-Addition-LLM-POC", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PhysiQuanty/Binary-Addition-LLM-POC with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PhysiQuanty/Binary-Addition-LLM-POC

SGLang

How to use PhysiQuanty/Binary-Addition-LLM-POC 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 "PhysiQuanty/Binary-Addition-LLM-POC" \
    --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": "PhysiQuanty/Binary-Addition-LLM-POC",
		"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 "PhysiQuanty/Binary-Addition-LLM-POC" \
        --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": "PhysiQuanty/Binary-Addition-LLM-POC",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PhysiQuanty/Binary-Addition-LLM-POC with Docker Model Runner:
```
docker model run hf.co/PhysiQuanty/Binary-Addition-LLM-POC
```

PhysiQuanty commited on Feb 5

Commit

1f5048c

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1 Parent(s): 0d0c294

Update README.md

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@@ -16,6 +16,8 @@ tags:
 - addition
 - TinyTransformerLM
 license: apache-2.0
 ---
 # Binary-Calculator-LLM (Proof of Concept)
@@ -92,5 +94,4 @@ python3 inference_binary_calculator3.py \
 * Inputs are **10-bit integers** (0..1023). The output can exceed 10 bits (e.g. addition overflow), so the `BOR..EOR` block is decoded with **variable bit-length**.
 * The model is **tokenizer-free** in the sense that it operates directly on bits and a tiny set of structural tokens.
-* This is a **POC**: sampling settings (`temperature`, `top_k`) can affect stability. For deterministic behavior, you can lower temperature and/or increase constraints.

 - addition
 - TinyTransformerLM
 license: apache-2.0
+datasets:
+- PhysiQuanty/BINARY-CARRY-PROPAGATION
 ---
 # Binary-Calculator-LLM (Proof of Concept)
 * Inputs are **10-bit integers** (0..1023). The output can exceed 10 bits (e.g. addition overflow), so the `BOR..EOR` block is decoded with **variable bit-length**.
 * The model is **tokenizer-free** in the sense that it operates directly on bits and a tiny set of structural tokens.
+* This is a **POC**: sampling settings (`temperature`, `top_k`) can affect stability. For deterministic behavior, you can lower temperature and/or increase constraints.