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")# pip install -U transformers accelerate # 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
Commit History
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Update handoff with on-policy error-targeted reasoning SFT v4
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V4 final rejection after protected information and reasoning gates
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V4 final rejection after protected information and reasoning gates
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V4 final rejection after protected information and reasoning gates
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V4 final rejection after protected information and reasoning gates
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V4 recoverable on-policy error-targeted SFT source
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V4 recoverable on-policy error-targeted SFT source
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V4 error-targeted SFT dataset and leader anchoring
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V4 error-targeted SFT dataset and leader anchoring
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V4 error-targeted SFT dataset and leader anchoring
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V4 scored on-policy frontier and verified minimal corrections
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V4 scored on-policy frontier and verified minimal corrections
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V4 scored on-policy frontier and verified minimal corrections
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V4 scored on-policy frontier and verified minimal corrections
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Complete V4 on-policy rollout generation 300 problems / 600 trajectories
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V4 on-policy rollout checkpoint 250/300
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V4 on-policy rollout checkpoint 200/300
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V4 on-policy rollout checkpoint 150/300
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V4 on-policy rollout checkpoint 100/300
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V4 on-policy rollout checkpoint 50/300
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Bootstrap on-policy error-targeted reasoning v4
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Bootstrap on-policy error-targeted reasoning v4
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Bootstrap on-policy error-targeted reasoning v4
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Confirm v3 rejection with extended fixed-MATH alpha line search
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Confirm v3 rejection with extended fixed-MATH alpha line search
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Confirm v3 rejection with extended fixed-MATH alpha line search
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Update handoff with long reasoning SFT before RL v3
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Add v3 evaluation artifact v3_reasoning_sft_manifest.json
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Add v3 evaluation artifact v3_fixed_scores.json
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Add v3 evaluation artifact v3_math_dev_scores.json
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Add v3 evaluation artifact v3_info_dev.json
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Add v3 evaluation artifact V3_FINAL_DECISION.json
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Reasoning SFT then RL v3: final Stage A rejection report
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Add reasoning SFT v3 encoding manifest
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Add reasoning SFT v3 training report
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Reasoning SFT v3: recoverable long verified SFT source
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Add reasoning SFT then constrained RL v3 policy
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Add reasoning SFT v3 data manifest
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Add 208-row held-out verified math calibration split
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Bootstrap reasoning SFT v3 with 800 verified long math solutions
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Update handoff with math-augmented constrained information RL v2
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Math-augmented constrained RL v2: final rejection after fixed no-regression gate
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Math-augmented RL v2: constrained joint source with gradient projection
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Math-augmented RL v2: add leader log-probabilities for joint constrained training
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Math-augmented RL v2: add 96 scored math groups and symbolic rewards
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Bootstrap math-augmented information RL v2 with 96 verified decontaminated math rows
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Update Budgie handoff with information-management RL v1 result
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Information RL v1 hygiene: remove superseded and rejected full checkpoints
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Information-management RL v1: final rejection after protected reasoning gate
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Information RL v1: preliminary alpha-0.025 candidate and held-out decision
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