Text Generation
Transformers
Safetensors
English
testgeniy
causal-lm
reasoning
mathematics
logic
long-context
4k-context
small-language-model
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
Add TestGeniy v6 dialogue SFT candidate metadata
Browse files- dialogue_sft_v6/README.md +20 -0
dialogue_sft_v6/README.md
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# TestGeniy v6 Dialogue SFT
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This folder contains the accepted dialogue-focused SFT candidate for TestGeniy. It is uploaded separately and does not replace the repository root model files.
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## Provenance
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- Base: `Asilarkness/testgeniy@116679b75b635dcbb0d21d068b74fcafc599947b`
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- Real data only; no synthetic answers and no MTP
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- `OpenAssistant/oasst1`: human-authored and human-ranked dialogue
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- `nvidia/HelpSteer2`: human quality annotations and human preference data
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## Training
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Full-parameter SFT, AdamW, 420 steps, micro-batch 2, gradient accumulation 8, learning rate `5e-7`, maximum sequence length 768.
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## Evaluation
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On the fixed 100-example suite, the candidate changed the aggregate from **19.75** to **21.5**: GSM8K 20>24, MATH-500 4>7, ARC-Challenge 21>26, FOLIO 34>29. It is therefore accepted as a dialogue-focused candidate, but not as an unconditional universal replacement: FOLIO and separate human dialogue preference holdouts regressed.
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See `manifest.json` for exact metadata.
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