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 config.json from CyanMonkey/Test-Subject-3: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/config.json
- Command line
-
hf download hf://CyanMonkey/Test-Subject-3/config.json
-
curl -L -o config.json https://huggingface.co/CyanMonkey/Test-Subject-3/resolve/main/config.json
2.48 kB
| { | |
| "architectures": [ | |
| "OleanderForCausalLM" | |
| ], | |
| "model_type": "oleander", | |
| "vocab_size": 32770, | |
| "hidden_size": 576, | |
| "num_hidden_layers": 28, | |
| "oleander_layer_types": [ | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa", | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa" | |
| ], | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "intermediate_size": 1600, | |
| "max_position_embeddings": 1024, | |
| "rms_norm_eps": 1e-06, | |
| "num_attention_heads": 9, | |
| "num_key_value_heads": 3, | |
| "head_dim": 64, | |
| "tie_word_embeddings": true, | |
| "use_cache": false, | |
| "attn_implementation": "sdpa", | |
| "oleander": { | |
| "arch_name": "OleanderForCausalLM", | |
| "vocab_size": 32770, | |
| "hidden_size": 576, | |
| "num_groups": 7, | |
| "group_pattern": [ | |
| "gdn2", | |
| "gdn2", | |
| "gdn2", | |
| "gqa" | |
| ], | |
| "mlp_hidden": 1600, | |
| "max_seq_len": 1024, | |
| "norm_eps": 1e-06, | |
| "init_std": 0.02, | |
| "tie_embeddings": true, | |
| "attn_res": true, | |
| "residual_dtype": "bfloat16", | |
| "gdn2": { | |
| "heads": 9, | |
| "head_dim": 64, | |
| "conv_size": 4, | |
| "norm_eps": 1e-05, | |
| "allow_neg_eigval": false, | |
| "gate_in_kernel": false | |
| }, | |
| "gqa": { | |
| "q_heads": 9, | |
| "kv_heads": 3, | |
| "head_dim": 64, | |
| "rotary_dim": 32, | |
| "rope_theta": 100000.0, | |
| "qk_norm": true, | |
| "value_residual": true | |
| } | |
| }, | |
| "model_name": "Oleander-150M", | |
| "training": { | |
| "step": 6500, | |
| "kind": "export", | |
| "max_steps": 28820, | |
| "percent_complete": 22.554, | |
| "tokens_seen": 20786970624, | |
| "compute_tokens_seen": 3464495104 | |
| }, | |
| "transformers_version": "5.14.1", | |
| "torch_dtype": "float32" | |
| } |