Instructions to use Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3 with Transformers:

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

pipe = pipeline("text-generation", model="Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3")
model = AutoModelForCausalLM.from_pretrained("Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3

SGLang

How to use Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3 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 "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3" \
    --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": "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3",
		"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 "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3" \
        --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": "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3 with Docker Model Runner:
```
docker model run hf.co/Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3
```

function_calling_TinyLlama-1.1B-python-v0.1_epoch3

function_calling_TinyLlama-1.1B-python-v0.1_epoch3 is an SFT fine-tuned version of TinyLlama/TinyLlama-1.1B-step-50K-105b using a custom training dataset. This model was made with Phinetune

Process

Learning Rate: 1.41e-05
Maximum Sequence Length: 2048
Dataset: Inishds/function_calling
Split: train

💻 Usage

!pip install -qU transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = "Inishds/function_calling_TinyLlama-1.1B-python-v0.1_epoch3"
tokenizer = AutoTokenizer.from_pretrained(model)

# Example prompt
prompt = "Your example prompt here"

# Generate a response
model = AutoModelForCausalLM.from_pretrained(model)
pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
print(outputs[0]["generated_text"])

Downloads last month: 8

Safetensors

Model size

1B params

Tensor type

F16