Text Generation
PEFT
Safetensors
French
text2sql
text-to-sql
lora
qlora
schema-linking
qwen2.5-coder
french
conversational
Instructions to use axrafTic/text2sqlProspeciton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use axrafTic/text2sqlProspeciton with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "axrafTic/text2sqlProspeciton") - Notebooks
- Google Colab
- Kaggle
Delete config.json with huggingface_hub
Browse files- config.json +0 -34
config.json
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{
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"classifier_dropout": 0.0,
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"d_ff": 3584,
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"d_kv": 64,
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"d_model": 1472,
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"dtype": "float32",
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
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"gradient_checkpointing": false,
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"initializer_factor": 1.0,
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"is_decoder": false,
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"is_encoder_decoder": true,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"num_decoder_layers": 4,
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"num_heads": 6,
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"num_layers": 12,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"scale_decoder_outputs": true,
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"tie_word_embeddings": false,
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"tokenizer_class": "ByT5Tokenizer",
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"transformers_version": "5.0.0",
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"use_cache": true,
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"vocab_size": 384
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}
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