metadata
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 python_code_instructions_18k_alpaca Code instructions dataset by using the method QLoRA in 4-bit with PEFT library.
Pretrained description
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
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
# 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
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 }
}