Instructions to use CyanMonkey/Test-Subject-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CyanMonkey/Test-Subject-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CyanMonkey/Test-Subject-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("CyanMonkey/Test-Subject-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CyanMonkey/Test-Subject-3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CyanMonkey/Test-Subject-3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CyanMonkey/Test-Subject-3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CyanMonkey/Test-Subject-3
- SGLang
How to use CyanMonkey/Test-Subject-3 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 "CyanMonkey/Test-Subject-3" \ --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": "CyanMonkey/Test-Subject-3", "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 "CyanMonkey/Test-Subject-3" \ --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": "CyanMonkey/Test-Subject-3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CyanMonkey/Test-Subject-3 with Docker Model Runner:
docker model run hf.co/CyanMonkey/Test-Subject-3
Download progress.json from CyanMonkey/Test-Subject-3: direct link, hf CLI and curl.
- Browser
- Download file 810 Bytes
-
https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/progress.json
- Command line
-
hf download hf://CyanMonkey/Test-Subject-3/progress.json
-
curl -L -o progress.json https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/progress.json
810 Bytes
| { | |
| "step": 6500, | |
| "max_steps": 28820, | |
| "percent_complete": 22.554, | |
| "tokens_seen": 20786970624, | |
| "compute_tokens_seen": 3464495104, | |
| "compute_tokens_total": 15109980160, | |
| "percent_tokens": 22.929, | |
| "val_loss": 5.523073740303516, | |
| "val_ppl": 250.40353093471919, | |
| "params_M": 149.976, | |
| "pushed_at": "2026-11-13T07:27:27Z", | |
| "architecture": "Oleander-150M", | |
| "features": [ | |
| "GDN2 (gated delta net 2) x21: 9h x 64, conv4", | |
| "gated GQA x7: 9 Q / 3 KV heads, head_dim 64", | |
| "QK-Norm (offset form)", | |
| "partial RoPE (rotary_dim 32, theta 100000)", | |
| "value residual (v_mix)", | |
| "SwiGLU", | |
| "RMSNorm", | |
| "AttnRes (block attention residuals)", | |
| "tied embeddings", | |
| "TST (token superposition training, bag 6)" | |
| ], | |
| "optimizer": "muon_adamw", | |
| "phase": "tst", | |
| "kind": "export" | |
| } |