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 TestGeniy v6 dialogue SFT candidate metadata
Browse files
dialogue_sft_v6/manifest.json
ADDED
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{
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"candidate": "testgeniy-v6-dialogue-sft",
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"status": "accepted dialogue-focused candidate; not universal main checkpoint",
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"source_repo": "Asilarkness/testgeniy",
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"source_commit": "116679b75b635dcbb0d21d068b74fcafc599947b",
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"source_checkpoint": "resume/structured_sft_latest.pt",
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"real_only": true,
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"synthetic": false,
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"mtp": false,
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"training": {
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"steps": 420,
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"micro_batch": 2,
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"grad_accum": 8,
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"learning_rate": 5e-07,
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"max_len": 768,
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"seed": 20260819,
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"optimizer": "AdamW",
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"full_parameter_finetune": true,
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"sources": {
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"OpenAssistant/oasst1": 10791,
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"nvidia/HelpSteer2": 8996
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}
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},
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"evaluation": {
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"fixed_suite_n": 100,
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"original": {
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"gsm8k_test": 20.0,
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"math500_test": 4.0,
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"arc_challenge_test": 21.0,
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"folio_validation": 34.0,
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"composite": 19.75
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},
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"dialogue_sft": {
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"gsm8k_test": 24.0,
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"math500_test": 7.0,
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"arc_challenge_test": 26.0,
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"folio_validation": 29.0,
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"composite": 21.5
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},
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"dialogue_candidate_gate": "passed aggregate gate",
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"helpsteer2_human_rated_pair_accuracy": {
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"original": 46.00484261501211,
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"candidate": 45.27845036319613,
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"n": 413
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},
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"oasst_human_preference_accuracy": {
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"original": 58.0,
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"candidate": 55.0,
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"n": 100
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}
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},
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"files": {
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"dialogue_sft.pt": "PyTorch checkpoint for the custom TestGeniy architecture; load with the project code."
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}
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}
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