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
metadata
library_name: pytorch
tags:
- causal-lm
- reinforcement-learning
- human-feedback
- testgeniy
- custom-architecture
language:
- en
- ru
base_model: Asilarkness/testgeniy
TestGeniy RL v2 - HelpSteer2/OASST real-RL step 20
Experimental post-training checkpoint for the custom TestGeniy architecture. This is a separate candidate folder; it does not replace the primary dialogue_sft_v6 checkpoint.
Training
- Base: testgeniy_v6_clean/checkpoints/dialogue_sft.pt
- Algorithm: online group-relative policy gradient with a KL anchor to v6
- Human reward: reward head trained on human HelpSteer2 ratings and validated on OASST preferences
- Verifier rewards: real GSM8K, MATH, ARC-Challenge and FOLIO labels
- Prompts: real-only; no synthetic prompt dataset
- MTP: disabled
- Updates: 20
- Group size: 4
- Learning rate: 5e-8
Evaluation
Same fixed 100-example suite and evaluator used for the v6 comparison:
| Checkpoint | GSM8K | MATH-500 | ARC | FOLIO | Composite |
|---|---|---|---|---|---|
| v6 Dialogue SFT | 24 | 7 | 26 | 29 | 21.50 |
| RL v2 step 20 | 24 | 8 | 26 | 29 | 21.75 |
Human holdouts: OASST validation 55% for both; HelpSteer2-derived 500-pair holdout 41.6% for both.
This checkpoint is an experimental candidate. Validate before replacing the primary model.