Instructions to use Bochkov/llm-fix-min-fixed-minimal-binary-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with Transformers:

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

pipe = pipeline("text-generation", model="Bochkov/llm-fix-min-fixed-minimal-binary-code", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Bochkov/llm-fix-min-fixed-minimal-binary-code", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bochkov/llm-fix-min-fixed-minimal-binary-code

SGLang

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code 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 "Bochkov/llm-fix-min-fixed-minimal-binary-code" \
    --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": "Bochkov/llm-fix-min-fixed-minimal-binary-code",
		"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 "Bochkov/llm-fix-min-fixed-minimal-binary-code" \
        --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": "Bochkov/llm-fix-min-fixed-minimal-binary-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with Docker Model Runner:
```
docker model run hf.co/Bochkov/llm-fix-min-fixed-minimal-binary-code
```

Bochkov commited on 1 day ago

Commit

903ca8e

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1 Parent(s): f680f51

Update README.md

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@@ -90,4 +90,22 @@ This model is a research checkpoint. It is not intended for deployment. It may p
 ## Training data
-The model was trained on the same FineWeb-Edu + Cosmopedia mixture used for the matched comparisons in the paper. Dataset terms and licenses are those of the original datasets.

 ## Training data
+The model was trained on the same FineWeb-Edu + Cosmopedia mixture used for the matched comparisons in the paper. Dataset terms and licenses are those of the original datasets.
+---
+## 🧑‍🔬 Citation & Concept
+If you use this model or the underlying concepts in your research, please cite our work:
+```
+@misc{bochkov2026languagemodelstrainableinput,
+      title={Language Models Without a Trainable Input Embedding Table: Learning from Fixed Minimal Binary Token Codes},
+      author={A. Bochkov},
+      year={2026},
+      eprint={2605.09751},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2605.09751},
+}
+```