Instructions to use ed001/datascience-coder-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ed001/datascience-coder-1.3b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ed001/datascience-coder-1.3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ed001/datascience-coder-1.3b")
model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-1.3b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ed001/datascience-coder-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ed001/datascience-coder-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ed001/datascience-coder-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ed001/datascience-coder-1.3b

SGLang

How to use ed001/datascience-coder-1.3b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ed001/datascience-coder-1.3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ed001/datascience-coder-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ed001/datascience-coder-1.3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ed001/datascience-coder-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ed001/datascience-coder-1.3b with Docker Model Runner:
```
docker model run hf.co/ed001/datascience-coder-1.3b
```

datascience-coder-1.3b

File size: 2,310 Bytes

---
license: cc-by-nc-sa-4.0
datasets:
- ed001/ds-coder-instruct-v1
language:
- en
pipeline_tag: text-generation
tags:
- code
- data science
---

# 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-1.3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-1.3b", 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  

Training was performed on the python subset of the ds-coder-instruct dataset.

## Examples


<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/d3qCHXdrNNlq4VMus7e_S.png" width="90%"/>

<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/pU7flGRav_h1WDCj12RwP.png" width="90%"/>

<img src="https://cdn-uploads.huggingface.co/production/uploads/62618f3e6dae705b2567fb13/txFZANcIhaY-6mEe49kTE.png" width="90%"/>

## Contact
GitHub: [Ea0011](https://github.com/Ea0011)