Instructions to use rahuldshetty/tinyllama-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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
```

tinyllama-python

File size: 1,109 Bytes

---
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 cube of a number.
      
      ### Response:
  - text: |-
      ### Instruction:
      Write a function to find factorial.
      
      ### Response:
  - text: |-
      ### Instruction:
      Write a function to check whether a number is prime or not.
      
      ### 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:
```