Instructions to use Asilarkness/testgeniy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asilarkness/testgeniy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Asilarkness/testgeniy")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Asilarkness/testgeniy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Asilarkness/testgeniy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Asilarkness/testgeniy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Asilarkness/testgeniy
- SGLang
How to use Asilarkness/testgeniy 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 "Asilarkness/testgeniy" \ --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": "Asilarkness/testgeniy", "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 "Asilarkness/testgeniy" \ --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": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Asilarkness/testgeniy with Docker Model Runner:
docker model run hf.co/Asilarkness/testgeniy
TestGeniy 4K Context Reasoning Model
TestGeniy is a compact causal language model focused on mathematical reasoning, formal logic, and helpful text interaction.
This main release is the validated 4K-context anchor. It is the safe production checkpoint after context-extension and regression testing.
Release summary
- Context window: 4096 tokens.
- RoPE: extended from 2048 to 4096 positions using the original theta value 500000.
- Attention: sliding attention with block size 1024 and global attention in layers 3, 7, 11, 15, 19, and 23.
- Weights: validated
logic_small_scope_step080anchor, with context buffers extended to 4096. - Evaluation questions were kept out of training.
- This main release does not include the rejected synthetic-CoT candidates.
Validation
The 4K model remained finite on full 4096-token forward passes and answered a 3157-token long-context probe correctly.
Fixed paired reasoning gate, 12 examples per dataset:
| Benchmark | Anchor | 4K main |
|---|---|---|
| GSM8K | 2/12 | 2/12 |
| MATH-500 | 2/12 | 2/12 |
| ARC-Challenge | 5/12 | 5/12 |
| FOLIO | 4/12 | 4/12 |
The release is a verified context-capability improvement with no measured regression on this gate. It is not presented as a benchmark-accuracy improvement.
Intended use
Use this checkpoint for compact English reasoning experiments, long-context prompting up to 4096 tokens, and further controlled fine-tuning.
Limitations
This is a small research model. It can produce incorrect reasoning or answers, especially on difficult mathematics and formal logic. The benchmark gate above is a regression gate, not a broad capability estimate.
Provenance
Base checkpoint: logic_small_scope_step080 from this project. The published weights contain no benchmark questions and no synthetic-CoT training data.
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