Create README.md

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+- peft
+base_model: mistralai/Mistral-7B-v0.1
+datasets:
+- b-mc2/sql-create-context
+model-index:
+- name: mistral-7b-text-to-sql
+  results: []
+reference:
+- https://www.philschmid.de/fine-tune-llms-in-2024-with-trl
+language:
+- en
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mistral-7b-text-to-sql_full-model
+
+- This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the b-mc2/sql-create-context dataset.
+- These are the full model weights (merged with adapter weights), and the code to use these for generation is given below.
+
+## Model description
+
+- Model type: Language model
+- Language(s) (NLP): English
+- License: Apache 2.0
+- Finetuned from model : Mistral-7B-v0.1
+
+## How to get started with the model
+
+```python
+import torch
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model directly
+
+tokenizer = AutoTokenizer.from_pretrained("delayedkarma/mistral-7b-text-to-sql_full-model")
+model = AutoModelForCausalLM.from_pretrained("delayedkarma/mistral-7b-text-to-sql_full-model")
+
+text = "How many matched scored 3–6, 7–6(5), 6–3?"
+inputs = tokenizer(text, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=40)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 3
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 6
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 3
+
+### Framework versions
+
+- PEFT 0.7.2.dev0
+- Transformers 4.36.2
+- Pytorch 2.2.2
+- Datasets 2.16.1
+- Tokenizers 0.15.2