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README.md

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+---
+library_name: transformers
+tags:
+- code
+license: mit
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+pipeline_tag: text-generation
+language:
+- en
+---
+
+# PyCodeGen 350M
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is finetuned version of [codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) by Salesforce trained on python code [dataset](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) using QLORA method.
+
+
+## Pretrained model description
+
+[codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)
+
+Codegen-350M-mono comes from the family of autoregressive models for program synthesis developed by Salesforce. 
+This model was first trained on ThePile dataset which is 825.18 GiB English text corpus.
+It was then adapted to generate code by training on a set of GitQuery with source codes.
+Finally model has been adapted to the Python language by training on the BigPython dataset.
+
+
+## Training Data
+
+[python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
+
+The dataset contains problem descriptions and code in python language. 
+This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style.
+
+## Intended uses
+
+The model can be used to generate python code that solves task with optionally given input data.
+
+
+## Example of usage
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+model = AutoModelForCausalLM.from_pretrained('chincyk/PyCodeGen')
+tokenizer = AutoTokenizer.from_pretrained('chincyk/PyCodeGen')
+
+instruction = "Write a python class that represents a calculator, then use it to add two numbers."
+input = "a = 5, b = 2"
+
+prompt = f"""
+    ### Instruction:
+    Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.
+
+    ### Task:
+    {instruction}
+
+    ### Input:
+    {input}
+    
+    ### Response:
+    """
+
+input_ids = tokenizer(prompt, truncation=True, return_tensors="pt")['input_ids']
+output = model.generate(input_ids=input_ids, max_length=200)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+```
+
+## Training parameters
+
+BitsAndBytes:
+- load_in_4bit: True,
+- bnb_4bit_quant_type: nf4,
+- bnb_4bit_use_double_quant: True,
+- bnb_4bit_compute_dtype: torch.bfloat16
+
+LoraConfig:
+- r: 32,
+- lora_alpha: 16,
+- target_modules: all-linear,
+- lora_dropout: 0.1,
+- bias: none,
+- task_type: CASUAL_LM
+
+Finetuning:
+- num_epochs: 15
+- train_batch_size: 4
+- eval_batch_size: 8
+- gradient_accumulation_steps: 8
+- learning_rate: 3e-4
+- weight_decay: 0.01
+- lr_scheduler_name: cosine
+- num_warmup_steps: 190

added_tokens.json

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config.json

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+{
+  "_name_or_path": "Salesforce/codegen-350M-mono",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "CodeGenForCausalLM"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "codegen",
+  "n_ctx": 2048,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 20,
+  "n_positions": 2048,
+  "resid_pdrop": 0.0,
+  "rotary_dim": 32,
+  "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,
+      "temperature": 1.0
+    }
+  },
+  "tie_word_embeddings": false,
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.1",
+  "use_cache": true,
+  "vocab_size": 51200
+}

generation_config.json

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+  "bos_token_id": 1,
+  "eos_token_id": 50256,
+  "transformers_version": "4.37.1"
+}

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special_tokens_map.json

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+    "lstrip": false,
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+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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