Adding Evaluation Results (#1)

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	---
	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\|