README.md

metadata

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 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