Text Generation
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text-generation-inference
Instructions to use ed001/datascience-coder-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") 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
- 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
Update README.md
Browse filesFill in model details
README.md
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tags:
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- code
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---
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# The Data Science Coder
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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.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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def build_instruction_prompt(instruction):
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return '''
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You are the Data Science Coder, a helpful AI assistant created by a man named Ed.
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You help people with data science coding and you answer questions about data science in a helpful manner.
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### Instruction:
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{}
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### Response:
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'''.format(instruction.strip()).lstrip()
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tokenizer = AutoTokenizer.from_pretrained("ed001/datascience-coder-1.3b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-1.3b", trust_remote_code=True).cuda()
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pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=1024, top_p=0.95)
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result = pipe(build_instruction_prompt("Perform EDA on the Iris dataset"))
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print(result[0]['generated_text'])
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```
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## Contact
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GitHub: [Ea0011](https://github.com/Ea0011)
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