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+---
+tags:
+- generated_from_trainer
+- code
+- coding
+- llama-2
+model-index:
+- name: Llama-2-7b-python-coder
+  results: []
+license: apache-2.0
+language:
+- code
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+pipeline_tag: text-generation
+---
+
+
+# LlaMa 2 7B Python Coder using Unsloth 👩‍💻 
+
+**LlaMa-2 7b** fine-tuned on the **python_code_instructions_18k_alpaca Code instructions dataset** by using the library [Unsloth](https://github.com/unslothai/unsloth).
+
+
+## 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
+
+**SFTTrainer arguments**
+```py
+# Model Parameters
+max_seq_length = 2048
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+# LoRA Parameters
+r = 16
+target_modules = ["gate_proj", "up_proj", "down_proj"]
+#target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",],
+lora_alpha = 16
+
+# Training parameters
+learning_rate = 2e-4
+weight_decay = 0.01
+#Evaluation
+evaluation_strategy="no"
+eval_steps= 50
+
+# if training in epochs
+#num_train_epochs=2
+#save_strategy="epoch"
+
+# if training in steps
+max_steps = 1500
+save_strategy="steps"
+save_steps=500
+
+logging_steps=100
+warmup_steps = 10
+warmup_ratio=0.01
+batch_size = 4
+gradient_accumulation_steps = 4
+lr_scheduler_type = "linear"
+optimizer = "adamw_8bit"
+use_gradient_checkpointing = True
+random_state = 42
+```
+
+### Framework versions
+- Unsloth
+
+### Example of usage
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "edumunozsala/unsloth-llama-2-7B-python-coder"
+
+# Load the entire model on the GPU 0
+device_map = {"": 0}
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, torch_dtype=torch.float16, 
+                                             device_map="auto")
+
+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=0.3)
+
+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        = { unsloth-llama-2-7B-python-coder },
+	year         = 2024,
+	url          = { https://huggingface.co/edumunozsala/unsloth-llama-2-7B-python-coder },
+	publisher    = { Hugging Face }
+}
+```