---
language:
- en
license: cc-by-nc-sa-4.0
tags:
- code
- data science
datasets:
- ed001/ds-coder-instruct-v1
pipeline_tag: text-generation
model-index:
- name: datascience-coder-6.7b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 34.64
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 53.83
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 44.82
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 55.72
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 24.94
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ed001/datascience-coder-6.7b
      name: Open LLM Leaderboard
---

# 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](https://huggingface.co/datasets/ed001/ds-coder-instruct-v1) dataset which is constructed by filtering publicly available datasets on HuggingFace.

## Usage

```python
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
<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/0H8lj26xLOfLuCD0yVmER.png" width="90%"/>

<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/8W62qr1cPSLsq6lLfLCib.png" width="90%"/>

<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/XNLclcr4KQqtPseGg2Gzn.png" width="90%"/>

## Contact
GitHub: [Ea0011](https://github.com/Ea0011)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ed001__datascience-coder-6.7b)

|             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|