The Data Science Coder
Data Science coder is a group of fine tuned models designed to help with coding for data science applications. It comes in 2 variants: 1.3b and 6.7b. Models are fine tuned from DeepSeek Coder instruct versions. Fine tuning was performed on the ed001/ds-coder-instruct-v1 dataset which is constructed by filtering publicly available datasets on HuggingFace.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
def build_instruction_prompt(instruction):
return '''
You are the Data Science Coder, a helpful AI assistant created by a man named Ed.
You help people with data science coding and you answer questions about data science in a helpful manner.
### Instruction:
{}
### Response:
'''.format(instruction.strip()).lstrip()
tokenizer = AutoTokenizer.from_pretrained("ed001/datascience-coder-6.7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-6.7b", trust_remote_code=True).cuda()
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=1024, top_p=0.95)
result = pipe(build_instruction_prompt("Perform EDA on the Iris dataset"))
print(result[0]['generated_text'])
Training Details
lora_r: 16
lora_alpha: 8
lora_dropout: 0.05
target_modules: q, k, v, o, gate_proj, down_proj, up_proj, lm_head
weight_decay: 0
optmizer: paged_adamw_32bit
lr: 1e-4
lr_scheduler: cosine
max_seq_len: 4096
batch_size: 4
max_grad_norm: 0.5
warmup_ratio: 0.05
num_epochs: 1
The model was trained on the python susbet of the ds-coder-instruct dataset.
Samples
Contact
GitHub: Ea0011
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.99 |
| AI2 Reasoning Challenge (25-Shot) | 34.64 |
| HellaSwag (10-Shot) | 53.83 |
| MMLU (5-Shot) | 37.96 |
| TruthfulQA (0-shot) | 44.82 |
| Winogrande (5-shot) | 55.72 |
| GSM8k (5-shot) | 24.94 |