Instructions to use vishesh-t27/Nano-Llama-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vishesh-t27/Nano-Llama-Base with Transformers:

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

pipe = pipeline("text-generation", model="vishesh-t27/Nano-Llama-Base")

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

tokenizer = AutoTokenizer.from_pretrained("vishesh-t27/Nano-Llama-Base")
model = AutoModelForCausalLM.from_pretrained("vishesh-t27/Nano-Llama-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vishesh-t27/Nano-Llama-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vishesh-t27/Nano-Llama-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vishesh-t27/Nano-Llama-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vishesh-t27/Nano-Llama-Base

SGLang

How to use vishesh-t27/Nano-Llama-Base 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 "vishesh-t27/Nano-Llama-Base" \
    --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": "vishesh-t27/Nano-Llama-Base",
		"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 "vishesh-t27/Nano-Llama-Base" \
        --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": "vishesh-t27/Nano-Llama-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vishesh-t27/Nano-Llama-Base with Docker Model Runner:
```
docker model run hf.co/vishesh-t27/Nano-Llama-Base
```

Nano-Llama

A compact 67M parameter LLaMA-2-style language model pretrained on FineWeb dataset.

Model Details

Architecture: LLaMA-2-style transformer
Parameters: 678M
Training Data: FineWeb dataset (~100M tokens)
Context Length: 1024 tokens
Layers: 6
Hidden Size: 768
Attention Heads: 12

Training

Dataset: FineWeb (web-crawled high-quality text)
Tokens Trained: ~110M tokens
Training Time: ~6 hours on RTX 3090
Optimizer: AdamW
Learning Rate: 1e-4

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vishesh-t27/Nano-Llama")
model = AutoModelForCausalLM.from_pretrained("vishesh-t27/Nano-Llama")

model.eval()

# Test prompt
text = "The future of artificial intelligence is"
inputs = tokenizer(text, return_tensors="pt")

# Generate text
outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    temperature=0.8,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

# Decode and print
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Limitations

Small model size (67M parameters)
Limited training data compared to larger models
May generate repetitive or nonsensical text

License

MIT License

Downloads last month: 102

Safetensors

Model size

67.1M params

Tensor type

F32

vishesh-t27
/

Nano-Llama-Base