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
| { | |
| "release": "context4k_main", | |
| "checkpoint": "logic_small_scope_step080", | |
| "context_length": 4096, | |
| "rope_theta": 500000.0, | |
| "weights_updated": false, | |
| "benchmark_rows_used": false, | |
| "synthetic_rows_used": false, | |
| "gate": { | |
| "gsm8k": [ | |
| 2, | |
| 2 | |
| ], | |
| "math500": [ | |
| 2, | |
| 2 | |
| ], | |
| "arc_challenge": [ | |
| 5, | |
| 5 | |
| ], | |
| "folio": [ | |
| 4, | |
| 4 | |
| ] | |
| }, | |
| "hf_repo": "Asilarkness/testgeniy", | |
| "hub_path": "", | |
| "published_as": "main", | |
| "model_card": "README.md", | |
| "tags": [ | |
| "testgeniy", | |
| "text-generation", | |
| "causal-lm", | |
| "reasoning", | |
| "mathematics", | |
| "logic", | |
| "long-context", | |
| "4k-context", | |
| "small-language-model" | |
| ] | |
| } |