Instructions to use BEE-spoke-data/smol_llama-81M-tied with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/smol_llama-81M-tied with Transformers:

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

pipe = pipeline("text-generation", model="BEE-spoke-data/smol_llama-81M-tied")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/smol_llama-81M-tied")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/smol_llama-81M-tied")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BEE-spoke-data/smol_llama-81M-tied with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/smol_llama-81M-tied"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/smol_llama-81M-tied",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/smol_llama-81M-tied

SGLang

How to use BEE-spoke-data/smol_llama-81M-tied 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 "BEE-spoke-data/smol_llama-81M-tied" \
    --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": "BEE-spoke-data/smol_llama-81M-tied",
		"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 "BEE-spoke-data/smol_llama-81M-tied" \
        --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": "BEE-spoke-data/smol_llama-81M-tied",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/smol_llama-81M-tied with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/smol_llama-81M-tied
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

smol_llama-81M-tied

A small 81M param (total) decoder model, enabled through tying the input/output embeddings. This is the first version of the model.

768 hidden size, 6 layers
standard multi-head attention (24 heads), context length 1024
input/output embeddings are tied
train-from-scratch

Notes

This checkpoint is the 'raw' pre-trained model and has not been tuned to a more specific task. It should be fine-tuned before use in most cases.

slightly larger 101M param GQA pretrained version: here
For the chat version of this model, please see here

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.52
ARC (25-shot)	22.18
HellaSwag (10-shot)	29.33
MMLU (5-shot)	24.06
TruthfulQA (0-shot)	43.97
Winogrande (5-shot)	49.25
GSM8K (5-shot)	0.23
DROP (3-shot)	2.64

Downloads last month: 221

Safetensors

Model size

81.3M params

Tensor type

F32

Model tree for BEE-spoke-data/smol_llama-81M-tied

Adapters

3 models

Quantizations

2 models

Datasets used to train BEE-spoke-data/smol_llama-81M-tied

Collection including BEE-spoke-data/smol_llama-81M-tied

smol llama

Collection

🚧"raw" pretrained smol_llama checkpoints - WIP 🚧 • 4 items • Updated Apr 29, 2024 • 6