Text Generation
Transformers
Safetensors
English
gpt2
materials-science
crystallography
generative-ai
inverse-design
chemistry
text-generation-inference
Instructions to use c-bone/CrystaLLM-pi_density with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c-bone/CrystaLLM-pi_density with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="c-bone/CrystaLLM-pi_density")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PKVGPT tokenizer = AutoTokenizer.from_pretrained("c-bone/CrystaLLM-pi_density") model = PKVGPT.from_pretrained("c-bone/CrystaLLM-pi_density", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use c-bone/CrystaLLM-pi_density with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "c-bone/CrystaLLM-pi_density" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "c-bone/CrystaLLM-pi_density", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/c-bone/CrystaLLM-pi_density
- SGLang
How to use c-bone/CrystaLLM-pi_density 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 "c-bone/CrystaLLM-pi_density" \ --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": "c-bone/CrystaLLM-pi_density", "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 "c-bone/CrystaLLM-pi_density" \ --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": "c-bone/CrystaLLM-pi_density", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use c-bone/CrystaLLM-pi_density with Docker Model Runner:
docker model run hf.co/c-bone/CrystaLLM-pi_density
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// Data Arguments
//################
"dataset_HF": "c-bone/mattergen_den_ehull",
"pretrained_tokenizer_dir": "HF-cif-tokenizer",
"context_length": 1024,
"dataset_streaming": false,
// Filters
"remove_CIFs_above_context": true,
"remove_CIFs_with_unk": true,
// Conditional Arguments
//#######################
"condition_columns": "['norm_Density (g/cm^3)', 'norm_energy_above_hull']" ,
"n_prefix_tokens": 2,
"n_hidden_cond": 1024,
"cond_dropout": 0.01,
"share_layers": false,
"n_heads_sharing_slider": 2,
"cond_lr": 0.0005,
"cond_wd": 0.01,
"activate_conditionality": "PKV",
// Model Arguments
//#################
// Model Depth
// n_positions has been tied to context_length
"n_embd": 512,
"n_layer": 8,
"n_head": 8,
// Dropout
"residual_dropout": 0.1,
"embedding_dropout": 0.1,
"attention_dropout": 0.1,
// Trainer Arguments
//###################
// Batching
"train_batch_size": 32,
"eval_batch_size": 32,
"gradient_accumulation_steps": 1,
"auto_find_batch_size": false,
// Learning Rate and Optimizer
"learning_rate": 0.000005,
"lr_scheduler_type": "cosine_with_min_lr",
"lr_scheduler_kwargs": {
"min_lr_rate": 0.01
},
"warmup_ratio": 0.02, // 2% of training steps
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"grad_clip": 1.0,
// "max_grad_norm": 1.0,
"weight_decay": 0.01,
// Logging
"output_dir": "model_ckpts/mattergen_den_ehull/PKV_ft",
"save_total_limit": 2,
"report_to": "wandb",
"wandb_project_folder": "dataset_size_study",
"pretrained_model_dir": "model_ckpts/mpdb-small-base-lematerial/checkpoint-1250000",
"eval_strategy": "steps",
"eval_steps": 4000, // 0.5 epoch
"logging_steps": 50, // 0.1 epoch
"save_strategy": "steps",
"max_steps": 400000, // 30 epochs
// to calculate the number of steps, use the following formula:
// steps = (number of epochs) * (number of training samples) / (batch size)
"early_stopping_patience": 15,
"early_stopping_threshold": 0.000005,
// Utils
"seed": 2,
"data_seed": 1,
"load_best_model_at_end": true,
"metric_for_best_model": "eval_loss",
"greater_is_better": false,
"torch_compile": true,
"fp16": true,
"deepspeed_config": "_config_files/deepspeed_default.json",
// CodeCarbon Arguments
//######################
"codecarbon": true,
"tracker_project": "CrystaLLM-pi"
}
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