Instructions to use manojredhat/tiny-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manojredhat/tiny-llama with Transformers:

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

pipe = pipeline("text-generation", model="manojredhat/tiny-llama")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("manojredhat/tiny-llama")
model = AutoModelForCausalLM.from_pretrained("manojredhat/tiny-llama", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use manojredhat/tiny-llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/manojredhat/tiny-llama

SGLang

How to use manojredhat/tiny-llama 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 "manojredhat/tiny-llama" \
    --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": "manojredhat/tiny-llama",
		"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 "manojredhat/tiny-llama" \
        --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": "manojredhat/tiny-llama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use manojredhat/tiny-llama with Docker Model Runner:
```
docker model run hf.co/manojredhat/tiny-llama
```

tiny-llama / README.md

manojredhat

Correct Tiny LLaMA model metadata

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metadata

library_name: transformers
license: apache-2.0
language:
  - en
tags:
  - tinystories
  - llama
  - language-model
  - educational
  - safetensors
datasets:
  - roneneldan/TinyStories
model-index:
  - name: Tiny LLaMA
    results: []

Tiny LLaMA - TinyStories Edition

A small LLaMA-style causal language model trained on the TinyStories dataset. This repository contains the Hugging Face LlamaForCausalLM conversion of the local checkpoint from /home/manojk/small_llama/llama2.c/out/ckpt.pt.

Model Details

Model Type: Decoder-only Transformer (LlamaForCausalLM)
Parameters: 6,270,624
Layers: 6
Attention Heads: 6
Key/Value Heads: 6
Head Dimension: 48
Hidden Size: 288
Intermediate Size: 768
Vocabulary Size: 512
Training Sequence Length: 256
Data Type: float32
Format: safetensors

Training

Dataset: TinyStories
Training Iterations: 100
Initial Loss: 6.27
Final Loss: 4.81
Validation Loss: 6.29 to 4.77

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("manojredhat/tiny-llama")
model = AutoModelForCausalLM.from_pretrained("manojredhat/tiny-llama")

inputs = tokenizer("Once upon a time", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=40, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Tokenizer

The model uses a SentencePiece tokenizer with 512 tokens:

<unk>: token ID 0
<s>: token ID 1
</s>: token ID 2

Notes

This is an educational small model trained for short TinyStories-style text. It is not intended for production use, knowledge-intensive tasks, or long-form generation.