peft safetensors arxiv:1910.09700 base_model:bigcode/starcoderbase-3b license:bigcode-openrail-m region:us
---
library_name: peft
base_model: bigcode/starcoderbase-3b
license: bigcode-openrail-m
---

# 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]

<!-- Provide the basic links for the model. -->

- **Repository:** [arpieb/peft-lora-starcoderbase-3b-personal-copilot-elixir](https://huggingface.co/arpieb/peft-lora-starcoderbase-3b-personal-copilot-elixir)

## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

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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.

[More Information Needed]

## Training Details

### Training Data

[bigcode/the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup)

### Training Procedure 

Based on the finetuning workflow detailed in [Personal Copilot: Train Your Own Coding Assistant](https://huggingface.co/blog/personal-copilot), specifically the training code found under `personal_copilot/training` in the repo [pacman100/DHS-LLM-Workshop](https://github.com/pacman100/DHS-LLM-Workshop).

Script used to train the model:

```bash
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] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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

[More Information Needed]

### 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

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]


## 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
