Text Generation
Transformers
PyTorch
English
llama
code
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use rahuldshetty/tinyllama-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahuldshetty/tinyllama-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rahuldshetty/tinyllama-python")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rahuldshetty/tinyllama-python") model = AutoModelForCausalLM.from_pretrained("rahuldshetty/tinyllama-python") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rahuldshetty/tinyllama-python with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rahuldshetty/tinyllama-python" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rahuldshetty/tinyllama-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rahuldshetty/tinyllama-python
- SGLang
How to use rahuldshetty/tinyllama-python 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 "rahuldshetty/tinyllama-python" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rahuldshetty/tinyllama-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "rahuldshetty/tinyllama-python" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rahuldshetty/tinyllama-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rahuldshetty/tinyllama-python with Docker Model Runner:
docker model run hf.co/rahuldshetty/tinyllama-python
| license: apache-2.0 | |
| datasets: | |
| - iamtarun/python_code_instructions_18k_alpaca | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| tags: | |
| - code | |
| widget: | |
| - text: |- | |
| ### Instruction: | |
| Write a function to find square of a number. | |
| ### Response: | |
| - text: |- | |
| ### Instruction: | |
| Write a function to calculate factorial. | |
| ### Response: | |
| - text: |- | |
| ### Instruction: | |
| Write a function to check whether a number is prime. | |
| ### Response: | |
| # rahuldshetty/tinyllama-python-gguf | |
| - Base model: [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit) | |
| - Dataset: [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) | |
| - Training Script: [unslothai: Alpaca + TinyLlama + RoPE Scaling full example.ipynb](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | |
| ## Prompt Format | |
| ``` | |
| ### Instruction: | |
| {instruction} | |
| ### Response: | |
| ``` | |
| ## Example | |
| ``` | |
| ### Instruction: | |
| Write a function to find cube of a number. | |
| ### Response: | |
| ``` | |