Model Card for Model ID
First pass at finetuning bigcode/starcoderbase-7b
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]
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.
[More Information Needed]
Training Details
Training Data
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-7b" \
--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-7b-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: ~9h (actual run timing lost :facepalm:)
- 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]
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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
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Model tree for arpieb/peft-lora-starcoderbase-7b-personal-copilot-elixir
Base model
bigcode/starcoderbase-7b