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 reasoning SFT then constrained RL v3 policy
Browse files
candidates/budgie-alignment-v2/reasoning-sft-then-rl-v3/bootstrap/REASONING_SFT_THEN_RL_V3_POLICY.md
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# Budgie reasoning SFT → constrained RL v3
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## Principle
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Do not ask RL to create reasoning ability from sparse rewards. First build a stronger reasoning prior with long, verified SFT; only if SFT passes protected gates may information-management RL continue from that accepted SFT anchor.
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The architecture remains 487,800,064 parameters. The immutable recovery base is `verified-math-a025`.
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## Stage A — long reasoning SFT
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Training data:
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- at least 1,200 long, difficult, symbolically verified mathematics question/solution rows from unrelated allowed sources such as `open-r1/OpenR1-Math-220k`;
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- optional independently checkable formal-logic and general-reasoning rows from unrelated sources;
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- 105 manual information-management ideal dialogues as a small retention stream;
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- zero GSM8K, MATH-500, Hendrycks MATH, ARC, or FOLIO training rows.
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Filtering:
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- normalized exact match and shared 12-token windows against every fixed held-out prompt;
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- source-level benchmark-family bans;
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- deduplication;
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- length and repetition checks;
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- `math_verify` equivalence between the supplied reference solution and answer;
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- a separate held-out random-math calibration split.
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Optimization:
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- start from `verified-math-a025`;
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- FP32 master weights and BF16 autocast;
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- full-sequence assistant-only SFT;
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- frozen-leader KL anchoring;
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- curriculum from medium to hard and short to long;
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- early checkpoints and task-vector line search;
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- no optimizer state on the Hub.
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Stage A is accepted only if random verified mathematics improves and fixed GSM/MATH/ARC/FOLIO plus dialogue/repetition do not regress.
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## Stage B — constrained information RL
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Only after an accepted Stage A anchor:
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- reuse manual multi-turn information groups;
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- add verified math RL groups at a minimum 2:1 math-to-information ratio;
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- clipped token-level GRPO/PPO;
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- KL to the accepted SFT anchor and to the original leader;
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- project conflicting information gradients out of the protected math gradient;
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- rollback every 4–8 optimizer updates if any shadow capability gate falls.
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No public release unless the full fixed, expanded, HelpSteer, dialogue, repetition, and full-split gates are non-regressing.
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