Instructions to use damerajee/tinyllama-sft-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use damerajee/tinyllama-sft-small-v2 with Transformers:

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

pipe = pipeline("text-generation", model="damerajee/tinyllama-sft-small-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("damerajee/tinyllama-sft-small-v2")
model = AutoModelForCausalLM.from_pretrained("damerajee/tinyllama-sft-small-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use damerajee/tinyllama-sft-small-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "damerajee/tinyllama-sft-small-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "damerajee/tinyllama-sft-small-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/damerajee/tinyllama-sft-small-v2

SGLang

How to use damerajee/tinyllama-sft-small-v2 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 "damerajee/tinyllama-sft-small-v2" \
    --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": "damerajee/tinyllama-sft-small-v2",
		"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 "damerajee/tinyllama-sft-small-v2" \
        --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": "damerajee/tinyllama-sft-small-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use damerajee/tinyllama-sft-small-v2 with Docker Model Runner:
```
docker model run hf.co/damerajee/tinyllama-sft-small-v2
```

Steps to try the model:

prompt Template

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

load the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("damerajee/tinyllama-sft-small-v2")
model = AutoModelForCausalLM.from_pretrained("damerajee/tinyllama-sft-small-v2")

Inference

inputs = tokenizer(
[
    alpaca_prompt.format(
        "best places to visit in india", # instruction
        "", # input
        "", # output
    )
]*1, return_tensors = "pt")

outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
tokenizer.batch_decode(outputs)

Model Information

The base model unsloth/tinyllama-bnb-4bit was Instruct finetuned using Unsloth

Model Limitations

The model was trained on a very small dataset so it might not be as good ,will be training on larger dataset soon

Training Details

The model was trained for 1 epoch on a free goggle colab which took about 1 hour and 30 mins approximately

Downloads last month: 9

Safetensors

Model size

1B params

Tensor type

F16

Model tree for damerajee/tinyllama-sft-small-v2

Base model

unsloth/tinyllama-bnb-4bit

Finetuned

(214)

this model

damerajee
/

tinyllama-sft-small-v2