Text Generation
Transformers
PyTorch
Safetensors
English
llama
code
data science
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use ed001/datascience-coder-6.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ed001/datascience-coder-6.7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ed001/datascience-coder-6.7b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ed001/datascience-coder-6.7b") model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-6.7b", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ed001/datascience-coder-6.7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ed001/datascience-coder-6.7b" # 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-6.7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ed001/datascience-coder-6.7b
- SGLang
How to use ed001/datascience-coder-6.7b 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-6.7b" \ --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-6.7b", "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-6.7b" \ --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-6.7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ed001/datascience-coder-6.7b with Docker Model Runner:
docker model run hf.co/ed001/datascience-coder-6.7b
Commit History
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Adding `safetensors` variant of this model (#2)
Verified signature- Adding `safetensors` variant of this model (d81de4a15c768f2fbe659bef703fcd10585d8920) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>682c9e9
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Adding Evaluation Results (#1)
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# 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 = AutoModelF…9167a46
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