Upload README.md

README.md

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+---
+tags:
+- generated_from_trainer
+- code
+- coding
+- llama-2
+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 **CodeAlpaca 20k 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
+
+### Example of usage
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "mrm8488/llama-2-coder-7b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
+
+sample = dataset[randrange(len(dataset))]
+
+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:
+{sample['instruction']}
+
+### Input:
+{sample['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):]}")
+print(f"Ground truth:\n{sample['output']}")
+
+```
+
+### Citation
+
+```
+@misc {edumunozsala_2023,
+	author       = { {Eduardo Muñoz} },
+	title        = { llama-2-7b-int4-python-coder (Revision d30d193) },
+	year         = 2023,
+	url          = { https://huggingface.co/edumunozsala/llama-2-7b-int4-python-18k-alpaca },
+	doi          = { 10.57967/hf/0931 },
+	publisher    = { Hugging Face }
+}
+```