Instructions to use nilq/baby-python-mistral-1L-tiny-lua-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nilq/baby-python-mistral-1L-tiny-lua-ft with Transformers:

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

pipe = pipeline("text-generation", model="nilq/baby-python-mistral-1L-tiny-lua-ft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nilq/baby-python-mistral-1L-tiny-lua-ft")
model = AutoModelForCausalLM.from_pretrained("nilq/baby-python-mistral-1L-tiny-lua-ft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nilq/baby-python-mistral-1L-tiny-lua-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nilq/baby-python-mistral-1L-tiny-lua-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nilq/baby-python-mistral-1L-tiny-lua-ft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nilq/baby-python-mistral-1L-tiny-lua-ft

SGLang

How to use nilq/baby-python-mistral-1L-tiny-lua-ft 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 "nilq/baby-python-mistral-1L-tiny-lua-ft" \
    --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": "nilq/baby-python-mistral-1L-tiny-lua-ft",
		"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 "nilq/baby-python-mistral-1L-tiny-lua-ft" \
        --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": "nilq/baby-python-mistral-1L-tiny-lua-ft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nilq/baby-python-mistral-1L-tiny-lua-ft with Docker Model Runner:
```
docker model run hf.co/nilq/baby-python-mistral-1L-tiny-lua-ft
```

baby-python-mistral-1L-tiny-lua-ft

This model is a fine-tuned version of nilq/baby-python-mistral-1L-tiny-base on the nilq/small-lua-stack dataset. This is the Lua model in the paper Tracking Universal Features Through Fine-Tuning and Model Merging. It achieves the following results on the evaluation set:

Loss: 2.4518
Accuracy: 0.4941

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 1.0

Training results

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 8

Safetensors

Model size

35.1M params

Tensor type

F32

Model tree for nilq/baby-python-mistral-1L-tiny-lua-ft

Base model

nilq/baby-python-mistral-1L-tiny-base

Finetuned

(2)

this model

Merges

1 model

Dataset used to train nilq/baby-python-mistral-1L-tiny-lua-ft

Collection including nilq/baby-python-mistral-1L-tiny-lua-ft

Toy Models to Study

Collection

9 items • Updated Mar 17, 2024 • 2

Paper for nilq/baby-python-mistral-1L-tiny-lua-ft

Tracking Universal Features Through Fine-Tuning and Model Merging

Paper • 2410.12391 • Published Oct 16, 2024 • 6

Evaluation results

Accuracy on nilq/small-lua-stack
self-reported

0.494