Instructions to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")
model = AutoModelForCausalLM.from_pretrained("mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca

SGLang

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca 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 "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca" \
    --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": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"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 "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca" \
        --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": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with Docker Model Runner:
```
docker model run hf.co/mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca
```

tinyllama-bnb-4bit-ft-codeAlpaca

This model is a fine-tuned version of unsloth/tinyllama-bnb-4bit on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.7926

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: 4
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
1.1517	0.83	50	1.1598
0.9428	1.65	100	0.9327
0.8319	2.48	150	0.8448
0.8205	3.31	200	0.8102
0.7977	4.13	250	0.7966
0.763	4.96	300	0.7926

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

F32

BF16

Model tree for mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca

Base model

unsloth/tinyllama-bnb-4bit

Quantized

(145)

this model