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 training_meta.json from CyanMonkey/Test-Subject-3: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/training_meta.json
- Command line
-
hf download hf://CyanMonkey/Test-Subject-3/training_meta.json
-
curl -L -o training_meta.json https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/training_meta.json
5.24 kB
| { | |
| "format": "oleander-training-meta/1", | |
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| "kind": "export", | |
| "step": 6500, | |
| "tokens_seen": 20786970624, | |
| "pushed_at": "2026-11-13T07:27:27Z", | |
| "params_M": 149.976, | |
| "architecture": "Oleander-150M", | |
| "features": [ | |
| "GDN2 (gated delta net 2) x21: 9h x 64, conv4", | |
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| "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)" | |
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| "optimizer": "muon_adamw", | |
| "val_loss": 5.523073740303516, | |
| "val_ppl": 250.40353093471919, | |
| "run": { | |
| "model_name": "Oleander-150M", | |
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| "session_id": "20261112_195408_1a765b", | |
| "repo_id": "CyanMonkey/Test-Subject-3", | |
| "started_at": "2026-11-12T20:04:22Z", | |
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| "eta": "1d 18h 53m", | |
| "checkpoint_kind": "export" | |
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| "description": "MuonEq-R (muoneq_r, lr 0.02, 5 NS steps, momentum 0.95) + AdamW (lr 0.0015, betas (0.9, 0.95))", | |
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| "variant": "muoneq_r", | |
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| "ns_steps": 5, | |
| "weight_decay": 0.01 | |
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| "lr": 0.0015, | |
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| 0.9, | |
| 0.95 | |
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| "eps": 1e-08, | |
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| "grad_clip": 1.0, | |
| "schedule": { | |
| "name": "WSD", | |
| "warmup_steps": 100, | |
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| "decay_shape": "1-sqrt", | |
| "min_lr_ratio": 0.0, | |
| "decay_start_step": 23776 | |
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| "tst": { | |
| "enabled": true, | |
| "bag_size": 6, | |
| "phase_fraction": 0.3, | |
| "switch_step": 8646 | |
| } | |
| }, | |
| "model": { | |
| "params": 149976139, | |
| "params_M": 149.976, | |
| "num_layers": 28, | |
| "hidden_size": 576, | |
| "group_pattern": [ | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa" | |
| ], | |
| "num_groups": 7, | |
| "mlp_hidden": 1600, | |
| "vocab_size": 32770, | |
| "max_seq_len": 1024, | |
| "attn_res": true, | |
| "residual_dtype": "bfloat16", | |
| "tie_embeddings": true, | |
| "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)" | |
| ], | |
| "export_dtype": "float32" | |
| }, | |
| "data": { | |
| "dataset_repo_id": "CyanMonkey/NextGen-1-32-1024", | |
| "train_split": "train", | |
| "tokenizer_id": "AxiomicLabs/GPT-X2.5-135M", | |
| "n_train_rows": 14753933, | |
| "n_val_rows": 2048, | |
| "seq_len": 1024, | |
| "micro_batch": 32, | |
| "grad_accum_steps": 16, | |
| "doc_masking": "auto", | |
| "shuffle_block_rows": 192, | |
| "seed": 42, | |
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| }, | |
| "hardware": { | |
| "device": "cuda", | |
| "torch": "2.11.0+cu130", | |
| "amp_dtype": "torch.bfloat16", | |
| "preset": "full", | |
| "python": "3.13.11", | |
| "transformers": "5.14.1", | |
| "gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition", | |
| "vram_gb": 101.98, | |
| "peak_gb": 38.76 | |
| }, | |
| "checkpoint": { | |
| "kind": "export", | |
| "weights_file": "model.safetensors", | |
| "dtype": "float32", | |
| "config_file": "config.json", | |
| "auto_map": { | |
| "AutoConfig": "configuration_oleander.OleanderConfig", | |
| "AutoModelForCausalLM": "modeling_oleander.OleanderForCausalLM" | |
| }, | |
| "modeling_files": [], | |
| "tokenizer_files": [ | |
| "tokenizer.json", | |
| "tokenizer_config.json", | |
| "special_tokens_map.json", | |
| "vocab.json", | |
| "merges.txt", | |
| "added_tokens.json", | |
| "chat_template.jinja", | |
| "tokenizer.model" | |
| ] | |
| }, | |
| "diloco": { | |
| "enabled": false | |
| }, | |
| "extra": {} | |
| } |