Text Generation
Transformers
Safetensors
grt
code
python
tool-calling
function-calling
rotational-transformer
givens-rotations
clifford-algebra
matterwave
Instructions to use MatterWaveAI/grt-30m-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatterWaveAI/grt-30m-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatterWaveAI/grt-30m-code")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-30m-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatterWaveAI/grt-30m-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatterWaveAI/grt-30m-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatterWaveAI/grt-30m-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatterWaveAI/grt-30m-code
- SGLang
How to use MatterWaveAI/grt-30m-code 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 "MatterWaveAI/grt-30m-code" \ --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": "MatterWaveAI/grt-30m-code", "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 "MatterWaveAI/grt-30m-code" \ --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": "MatterWaveAI/grt-30m-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatterWaveAI/grt-30m-code with Docker Model Runner:
docker model run hf.co/MatterWaveAI/grt-30m-code
File size: 4,914 Bytes
6f6a759 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | {
"model_id": "MatterWaveAI/grt-30m-code",
"architecture": "GRTForCausalLM",
"pipeline_tag": "text-generation",
"base_model": "MatterWaveAI/grt-30m",
"parameters": 29305498,
"trainable_parameters": 811907,
"trainable_scope": "blocks[11:12] + final_norm + <|im_end|> phase calibration",
"nn_linear_layers": 0,
"nn_conv1d_layers": 0,
"steps": 200,
"batch_size": 16,
"learning_rate": 0.0035,
"seed": 42,
"best_optimizer": "huygens_adam",
"before_training": {
"val_loss": 4.0061,
"val_ppl": 54.9316,
"corrupted_prompt_val_loss": 4.9059,
"code_conditioning_margin": 0.8998,
"code_token_acc": 46.8,
"tool_call_token_acc": 36.44,
"json_schema_match_rate": 0.0,
"python_ast_syntax_validity": 0.0
},
"optimizers": {
"pair_adam": {
"optimizer_type": "pair_adam",
"variant": "PairAdam (Factored Toroidal Baseline)",
"final_train_loss": 1.0994,
"val_loss": 0.6018,
"val_ppl": 1.8253,
"code_token_acc": 97.6,
"tool_call_token_acc": 90.69,
"json_schema_match_rate": 83.33,
"python_ast_syntax_validity": 50.0,
"code_conditioning_margin": 1.6597,
"corrupted_prompt_val_loss": 2.2614,
"wall_time_s": 278.8
},
"hessian_pair_adam": {
"optimizer_type": "hessian_pair_adam",
"variant": "HessianPairAdam (Sophia-Torus Cauchy-Riemann Curvature)",
"final_train_loss": 1.2171,
"val_loss": 0.7292,
"val_ppl": 2.0734,
"code_token_acc": 97.87,
"tool_call_token_acc": 87.33,
"json_schema_match_rate": 66.67,
"python_ast_syntax_validity": 58.33,
"code_conditioning_margin": 1.4236,
"corrupted_prompt_val_loss": 2.1528,
"wall_time_s": 117.82
},
"polar_natural_adam": {
"optimizer_type": "polar_natural_adam",
"variant": "PolarNaturalAdam (Riemannian Polar Metric + Cauchy-Riemann Hessian)",
"final_train_loss": 1.2463,
"val_loss": 0.7891,
"val_ppl": 2.2015,
"code_token_acc": 96.93,
"tool_call_token_acc": 86.34,
"json_schema_match_rate": 66.67,
"python_ast_syntax_validity": 50.0,
"code_conditioning_margin": 1.4294,
"corrupted_prompt_val_loss": 2.2185,
"wall_time_s": 108.18
},
"cyclide_adam": {
"optimizer_type": "cyclide_adam",
"variant": "CyclideAdam (Dupin Cyclide Conic Metric + Deviatoric Twist)",
"final_train_loss": 1.1564,
"val_loss": 0.64,
"val_ppl": 1.8965,
"code_token_acc": 98.93,
"tool_call_token_acc": 89.5,
"json_schema_match_rate": 75.0,
"python_ast_syntax_validity": 83.33,
"code_conditioning_margin": 1.5727,
"corrupted_prompt_val_loss": 2.2127,
"wall_time_s": 141.92
},
"symplectic_adam": {
"optimizer_type": "symplectic_adam",
"variant": "SymplecticAdam (Gromov-Liouville Non-Squeezing Floor + Casimir Cavity)",
"final_train_loss": 1.2764,
"val_loss": 0.8445,
"val_ppl": 2.3268,
"code_token_acc": 97.6,
"tool_call_token_acc": 81.39,
"json_schema_match_rate": 50.0,
"python_ast_syntax_validity": 50.0,
"code_conditioning_margin": 1.5766,
"corrupted_prompt_val_loss": 2.421,
"wall_time_s": 138.1
},
"gyric_adam": {
"optimizer_type": "gyric_adam",
"variant": "GyricAdam (Poinsot-Lande Gyroscopic Projection + Thomas Precession)",
"final_train_loss": 1.1168,
"val_loss": 0.6216,
"val_ppl": 1.8619,
"code_token_acc": 98.53,
"tool_call_token_acc": 87.92,
"json_schema_match_rate": 75.0,
"python_ast_syntax_validity": 50.0,
"code_conditioning_margin": 1.5745,
"corrupted_prompt_val_loss": 2.1961,
"wall_time_s": 138.83
},
"huygens_adam": {
"optimizer_type": "huygens_adam",
"variant": "HuygensAdam (Circular Order Parameter R_p Splay Torque)",
"final_train_loss": 1.0882,
"val_loss": 0.5672,
"val_ppl": 1.7632,
"code_token_acc": 99.07,
"tool_call_token_acc": 90.69,
"json_schema_match_rate": 83.33,
"python_ast_syntax_validity": 91.67,
"code_conditioning_margin": 1.5152,
"corrupted_prompt_val_loss": 2.0824,
"wall_time_s": 138.63
},
"huygens_symplectic_adam": {
"optimizer_type": "huygens_symplectic_adam",
"variant": "HuygensSymplecticAdam (Two-Stage Huygens -> Symplectic Curriculum)",
"final_train_loss": 1.1819,
"val_loss": 0.6537,
"val_ppl": 1.9227,
"code_token_acc": 98.27,
"tool_call_token_acc": 89.31,
"json_schema_match_rate": 75.0,
"python_ast_syntax_validity": 66.67,
"code_conditioning_margin": 1.4057,
"corrupted_prompt_val_loss": 2.0594,
"wall_time_s": 122.82
}
}
}
|