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--- |
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library_name: peft |
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datasets: |
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- b-mc2/sql-create-context |
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language: |
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- en |
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metrics: |
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- rouge |
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pipeline_tag: question-answering |
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license: apache-2.0 |
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tags: |
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- SQL |
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- PEFT |
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- GPT |
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- GPT2-Medium |
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- Question& Answer |
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--- |
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# GPT-2 Medium |
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## Model Details |
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**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. |
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## Parameter-Efficient Fine-tuning (PEFT) |
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Parameter-Efficient Fine-tuning (PEFT) is a technique used to improve the performance of pre-trained language models (LLMs) on specific downstream tasks without fine-tuning all the model's parameters. This is done by freezing most of the model's parameters and only fine-tuning a small number of parameters that are specific to the downstream task. |
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## Training Data |
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the model is trained on 'b-mc2/sql-create-context' dataset upto 5000rows |
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## Usage: |
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please install `transformers`, and `peft`: |
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``` |
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!pip install transformers peft |
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``` |
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To use the model, you can run the following: |
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```py |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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config = PeftConfig.from_pretrained("Naveengo/gpt2-medium-on-sql-create-context") |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model,"Naveengo/gpt2-medium-on-sql-create-context") |
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from IPython.display import display, Markdown |
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def make_inference(question, context): |
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batch = tokenizer(f"Below is an SQL instruction that describes a task, paired with an input that provides further context. Write an SQL query that appropriately completes the request using your expertise in SQL. ### Instruction: {question}### Input: {context}### Response:", return_tensors='pt') |
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with torch.cuda.amp.autocast(): |
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output_tokens = model.generate(**batch, max_new_tokens=100) |
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display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True)))) |
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#give question and context to function |
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make_inference(your_question_here, your_context_here) |
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``` |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: float16 |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: float16 |
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### Framework versions |
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- PEFT 0.5.0 |
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- PEFT 0.5.0 |
