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
Reasoning SFT then RL v3: final Stage A rejection report
Browse files
candidates/budgie-alignment-v2/reasoning-sft-then-rl-v3/recovery/V3_FINAL_REPORT.md
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# Long reasoning SFT → RL v3
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## Decision
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**Stage A rejected; Stage B RL was not started. `verified-math-a025` remains the leader.**
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## Stage A
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- Scanned 5,251 unrelated OpenR1 rows.
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- Retained 1,008 decontaminated, symbolically verified long mathematics solutions.
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- Split 800 SFT train / 208 random-math development.
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- Added 108 manual information-management retention examples.
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- 908 encoded examples; average 577 tokens, maximum 1,536.
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- One full curriculum epoch, 227 updates, FP32 master weights, BF16 autocast.
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- Peak LR 3e-7 and sampled-action KL 0.12 to the immutable leader.
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- Zero benchmark-family training rows.
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## Results
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| Gate | Leader | SFT alpha .005 |
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| Information dev | 3/21 | **4/21** |
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| Information loops | 0 | 0 |
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| Random verified math dev | 1/32 | 1/32 |
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| GSM8K fixed | **5/30** | 4/30 |
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| MATH fixed | **3/15** | 2/15 |
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| ARC fixed | 11/30 | 11/30 |
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| FOLIO fixed | 13/30 | 13/30 |
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The SFT direction gives a small information gain, but does not improve random math and regresses fixed GSM/MATH. Smaller alphas do not improve held-out information management. Under the predeclared protocol, RL may run only after an accepted SFT anchor, so follow-on RL was correctly skipped.
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