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
Update handoff with math-augmented constrained information RL v2
Browse files
candidates/budgie-alignment-v2/README.md
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## 21. Information-management RL v1
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A manual long-dialogue RL attempt targeted state updates, stale-step avoidance, selective retrieval, delta summaries, conflict clarification, source boundaries, exact constraints, and repetition. It used 105 disjoint multi-turn cases, 252 stochastic Budgie rollouts, 63 manual ideal demonstrations, Qwen3.8 group rewards, clipped token-level GRPO/PPO with leader KL, a longer GRPO+BC run, and manual ideal-response consolidation. The full specialist source improved held-out information pass rates (dev 3/21 -> 7/21; test 1/21 -> 5/21) but increased loops. A low-drift alpha .025 improved dev to 4/21 and test to 2/21 without extra loops, then failed the protected reasoning gate: GSM 5/30 -> 3/30 and MATH 3/15 -> 1/15, with ARC 11/30 and FOLIO 13/30 unchanged. The branch is rejected; `verified-math-a025` remains active and the public root is unchanged. Read `information-rl-v1/recovery/FINAL_REPORT.md`. One reconstructable hybrid source is retained; superseded/rejected full checkpoints were removed from the current tree.
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## 21. Information-management RL v1
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A manual long-dialogue RL attempt targeted state updates, stale-step avoidance, selective retrieval, delta summaries, conflict clarification, source boundaries, exact constraints, and repetition. It used 105 disjoint multi-turn cases, 252 stochastic Budgie rollouts, 63 manual ideal demonstrations, Qwen3.8 group rewards, clipped token-level GRPO/PPO with leader KL, a longer GRPO+BC run, and manual ideal-response consolidation. The full specialist source improved held-out information pass rates (dev 3/21 -> 7/21; test 1/21 -> 5/21) but increased loops. A low-drift alpha .025 improved dev to 4/21 and test to 2/21 without extra loops, then failed the protected reasoning gate: GSM 5/30 -> 3/30 and MATH 3/15 -> 1/15, with ARC 11/30 and FOLIO 13/30 unchanged. The branch is rejected; `verified-math-a025` remains active and the public root is unchanged. Read `information-rl-v1/recovery/FINAL_REPORT.md`. One reconstructable hybrid source is retained; superseded/rejected full checkpoints were removed from the current tree.
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## 22. Math-augmented constrained information RL v2
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A second information RL attempt added 96 decontaminated, symbolically verified OpenR1 math problems, 192 stochastic Budgie math rollouts, 96 verified references, a 72/24 random-math train/dev split, and a 2:1 math-to-information update ratio. Training started from `verified-math-a025` and used leader KL, PPO clipping, reference BC, and global gradient projection that removed information-gradient components conflicting with math gradients (37.3% conflict rate). Alpha .01 improved information dev 3/21 -> 4/21 with no loops and random verified math dev 1/24 -> 2/24. It preserved fixed GSM 5/30 and ARC 11/30, but MATH fell 3/15 -> 2/15 and FOLIO 13/30 -> 12/30. Alpha .005 did not improve information pass rate and fell to MATH 1/15, FOLIO 12/30. All candidates were rejected. The method reduced damage versus v1 but was not Pareto-safe. See `information-rl-v2-math-augmented/recovery/V2_FINAL_REPORT.md`.
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