Text Generation
Transformers
Safetensors
English
multilingual
encoder-decoder
text2text-generation
code-to-docstring
code-summarization
code-documentation
code
python
java
huggingface
modernbert
gpt2
Instructions to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Shuu12121/CodeEncoderDecoderModel-Ghost-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large") model = AutoModelForSeq2SeqLM.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shuu12121/CodeEncoderDecoderModel-Ghost-large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shuu12121/CodeEncoderDecoderModel-Ghost-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Shuu12121/CodeEncoderDecoderModel-Ghost-large
- SGLang
How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large 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 "Shuu12121/CodeEncoderDecoderModel-Ghost-large" \ --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": "Shuu12121/CodeEncoderDecoderModel-Ghost-large", "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 "Shuu12121/CodeEncoderDecoderModel-Ghost-large" \ --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": "Shuu12121/CodeEncoderDecoderModel-Ghost-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with Docker Model Runner:
docker model run hf.co/Shuu12121/CodeEncoderDecoderModel-Ghost-large
File size: 2,764 Bytes
442e1e5 | 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 | {
"architectures": [
"EncoderDecoderModel"
],
"cache_dir": "./cache",
"decoder": {
"_attn_implementation_autoset": true,
"_name_or_path": "openai-community/gpt2-large",
"activation_function": "gelu_new",
"add_cross_attention": true,
"architectures": [
"GPT2LMHeadModel"
],
"attn_pdrop": 0.1,
"embd_pdrop": 0.1,
"initializer_range": 0.02,
"is_decoder": true,
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_ctx": 1024,
"n_embd": 1280,
"n_head": 20,
"n_inner": null,
"n_layer": 36,
"n_positions": 1024,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"task_specific_params": {
"text-generation": {
"do_sample": true,
"max_length": 50
}
},
"torch_dtype": "float32",
"use_cache": true,
"vocab_size": 50257
},
"decoder_start_token_id": 50256,
"encoder": {
"_attn_implementation_autoset": true,
"_name_or_path": "Shuu12121/CodeModernBERT-Ghost",
"architectures": [
"ModernBertForMaskedLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 50000,
"classifier_activation": "gelu",
"classifier_bias": false,
"classifier_dropout": 0.0,
"classifier_pooling": "cls",
"cls_token_id": 50281,
"decoder_bias": true,
"deterministic_flash_attn": false,
"embedding_dropout": 0.0,
"eos_token_id": 50001,
"global_attn_every_n_layers": 3,
"global_rope_theta": 160000.0,
"hidden_activation": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_cutoff_factor": 2.0,
"initializer_range": 0.02,
"intermediate_size": 3072,
"local_attention": 128,
"local_attention_rope_theta": 10000,
"local_attention_window": 128,
"local_rope_theta": 10000.0,
"max_position_embeddings": 2048,
"mlp_bias": false,
"mlp_dropout": 0.0,
"model_type": "modernbert",
"norm_bias": false,
"norm_eps": 1e-05,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"repad_logits_with_grad": false,
"rope_theta": 160000,
"sparse_pred_ignore_index": -100,
"sparse_prediction": false,
"torch_dtype": "float32",
"type_vocab_size": 2,
"vocab_size": 50004
},
"eos_token_id": 50256,
"is_encoder_decoder": true,
"model_type": "encoder-decoder",
"pad_token_id": 50256,
"torch_dtype": "float32",
"transformers_version": "4.51.1"
}
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