Create README.md

README.md

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+---
+tags:
+- generated_from_trainer
+- code
+- coding
+- llama-2
+- gptq
+model-index:
+- name: Llama-2-7b-4bit-python-coder
+  results: []
+license: apache-2.0
+language:
+- code
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+pipeline_tag: text-generation
+---
+
+# LlaMa 2 7b 4-bit Python Coder 👩‍💻 
+
+**LlaMa-2 7b** fine-tuned on the **python_code_instructions_18k_alpaca Code instructions dataset** by using the method **QLoRA** in 4-bit with [PEFT](https://github.com/huggingface/peft) library.
+
+## Pretrained description
+
+[Llama-2](https://huggingface.co/meta-llama/Llama-2-7b)
+
+Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.
+
+Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety
+
+## 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.
+
+### Training hyperparameters
+
+The following `bitsandbytes` quantization config was used during training:
+- load_in_8bit: False
+- load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float16
+
+**SFTTrainer arguments**
+```py
+    # Number of training epochs
+    num_train_epochs = 1
+    # Enable fp16/bf16 training (set bf16 to True with an A100)
+    fp16 = False
+    bf16 = True
+    # Batch size per GPU for training
+    per_device_train_batch_size = 4
+    # Number of update steps to accumulate the gradients for
+    gradient_accumulation_steps = 1
+    # Enable gradient checkpointing
+    gradient_checkpointing = True
+    # Maximum gradient normal (gradient clipping)
+    max_grad_norm = 0.3
+    # Initial learning rate (AdamW optimizer)
+    learning_rate = 2e-4
+    # Weight decay to apply to all layers except bias/LayerNorm weights
+    weight_decay = 0.001
+    # Optimizer to use
+    optim = "paged_adamw_32bit"
+    # Learning rate schedule
+    lr_scheduler_type = "cosine" #"constant"
+    # Ratio of steps for a linear warmup (from 0 to learning rate)
+    warmup_ratio = 0.03
+```
+### Framework versions
+- PEFT 0.4.0
+
+### Training metrics
+```
+{'loss': 1.044, 'learning_rate': 3.571428571428572e-05, 'epoch': 0.01}
+{'loss': 0.8413, 'learning_rate': 7.142857142857143e-05, 'epoch': 0.01}
+{'loss': 0.7299, 'learning_rate': 0.00010714285714285715, 'epoch': 0.02}
+{'loss': 0.6593, 'learning_rate': 0.00014285714285714287, 'epoch': 0.02}
+{'loss': 0.6309, 'learning_rate': 0.0001785714285714286, 'epoch': 0.03}
+{'loss': 0.5916, 'learning_rate': 0.00019999757708974043, 'epoch': 0.03}
+{'loss': 0.5861, 'learning_rate': 0.00019997032069768138, 'epoch': 0.04}
+{'loss': 0.6118, 'learning_rate': 0.0001999127875580558, 'epoch': 0.04}
+{'loss': 0.5928, 'learning_rate': 0.00019982499509519857, 'epoch': 0.05}
+{'loss': 0.5978, 'learning_rate': 0.00019970696989770335, 'epoch': 0.05}
+{'loss': 0.5791, 'learning_rate': 0.0001995587477103701, 'epoch': 0.06}
+{'loss': 0.6054, 'learning_rate': 0.00019938037342337933, 'epoch': 0.06}
+{'loss': 0.5864, 'learning_rate': 0.00019917190105869708, 'epoch': 0.07}
+{'loss': 0.6159, 'learning_rate': 0.0001989333937537136, 'epoch': 0.08}
+{'loss': 0.583, 'learning_rate': 0.00019866492374212205, 'epoch': 0.08}
+{'loss': 0.6066, 'learning_rate': 0.00019836657233204182, 'epoch': 0.09}
+{'loss': 0.5934, 'learning_rate': 0.00019803842988139374, 'epoch': 0.09}
+{'loss': 0.5836, 'learning_rate': 0.00019768059577053473, 'epoch': 0.1}
+{'loss': 0.6021, 'learning_rate': 0.00019729317837215943, 'epoch': 0.1}
+{'loss': 0.5659, 'learning_rate': 0.00019687629501847898, 'epoch': 0.11}
+{'loss': 0.5754, 'learning_rate': 0.00019643007196568606, 'epoch': 0.11}
+{'loss': 0.5936, 'learning_rate': 0.000195954644355717, 'epoch': 0.12}
+```
+
+### Example of usage
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "edumunozsala/llama-2-7b-int4-python-code-20k"
+tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)
+model = AutoModelForCausalLM.from_pretrained(hf_model_repo, load_in_4bit=True, torch_dtype=torch.float16, 
+                                             device_map=device_map)
+instruction="Write a Python function to display the first and last elements of a list."
+input=""
+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, return_tensors="pt", truncation=True).input_ids.cuda()
+# with torch.inference_mode():
+outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5)
+print(f"Prompt:\n{prompt}\n")
+print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")
+```
+
+### Citation
+
+```
+@misc {edumunozsala_2023,
+	author       = { {Eduardo Muñoz} },
+	title        = { llama-2-7b-int4-python-coder },
+	year         = 2023,
+	url          = { https://huggingface.co/edumunozsala/llama-2-7b-int4-python-18k-alpaca },
+	publisher    = { Hugging Face }
+}
+```