arpieb/peft-lora-starcoderbase-3b-personal-copilot-elixir

Model Card for Model ID

First pass at finetuning bigcode/starcoderbase-3b on the Elixir language subset of bigcode/the-stack-dedup

Model Details

Model Description

• Developed by: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: arpieb/peft-lora-starcoderbase-3b-personal-copilot-elixir

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

bigcode/the-stack-dedup

Training Procedure

Based on the finetuning workflow detailed in Personal Copilot: Train Your Own Coding Assistant, specifically the training code found under personal_copilot/training in the repo pacman100/DHS-LLM-Workshop.

Script used to train the model:

python train.py \
    --model_path "bigcode/starcoderbase-3b" \
    --dataset_name "bigcode/the-stack-dedup" \
    --subset "data/elixir" \
    --data_column "content" \
    --split "train" \
    --seq_length 2048 \
    --max_steps 2000 \
    --batch_size 4 \
    --gradient_accumulation_steps 4 \
    --learning_rate 5e-4 \
    --lr_scheduler_type "cosine" \
    --weight_decay 0.01 \
    --num_warmup_steps 30 \
    --eval_freq 100 \
    --save_freq 100 \
    --log_freq 25 \
    --num_workers 4 \
    --bf16 \
    --no_fp16 \
    --output_dir "peft-lora-starcoderbase-3b-personal-copilot-rtx4090-elixir" \
    --push_to_hub "false" \
    --fim_rate 0.5 \
    --fim_spm_rate 0.5 \
    --use_flash_attn \
    --use_peft_lora \
    --lora_r 32 \
    --lora_alpha 64 \
    --lora_dropout 0.0 \
    --lora_target_modules "c_proj,c_attn,q_attn,c_fc,c_proj" \
    --use_4bit_qunatization \
    --use_nested_quant \
    --bnb_4bit_compute_dtype "bfloat16"

Preprocessing

N/A

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

NOTE the RTX-4090 is not available in the above estimator; will update once there is data available.

• Hardware Type: NVIDIA GeForce RTX 4090
• Hours used: 5h 20m 2s
• Cloud Provider: Local rig
• Compute Region: N/A
• Carbon Emitted: N/A

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

Hardware

Local DL rig with the following configuration:

• NVIDIA GeForce RTX 4090
• Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz
• 128GB RAM

Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• 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: True
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.6.2.dev0