Instructions to use MikeMpapa/lmd_8bar_final_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MikeMpapa/lmd_8bar_final_test with Transformers:

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

pipe = pipeline("text-generation", model="MikeMpapa/lmd_8bar_final_test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MikeMpapa/lmd_8bar_final_test")
model = AutoModelForCausalLM.from_pretrained("MikeMpapa/lmd_8bar_final_test")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MikeMpapa/lmd_8bar_final_test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MikeMpapa/lmd_8bar_final_test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MikeMpapa/lmd_8bar_final_test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MikeMpapa/lmd_8bar_final_test

SGLang

How to use MikeMpapa/lmd_8bar_final_test 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 "MikeMpapa/lmd_8bar_final_test" \
    --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": "MikeMpapa/lmd_8bar_final_test",
		"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 "MikeMpapa/lmd_8bar_final_test" \
        --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": "MikeMpapa/lmd_8bar_final_test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MikeMpapa/lmd_8bar_final_test with Docker Model Runner:
```
docker model run hf.co/MikeMpapa/lmd_8bar_final_test
```

lmd-8bars-2048-epochs40_v4

This model is a fine-tuned version of juancopi81/lmd-8bars-2048-epochs30_v4 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.9128

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: 0.0005
train_batch_size: 8
eval_batch_size: 4
seed: 1
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
0.959	0.5	4994	0.9735
0.9841	1.0	9988	0.9691
0.9779	1.5	14982	0.9632
0.9818	2.0	19976	0.9590
0.9736	2.5	24970	0.9610
0.9715	3.0	29964	0.9552
0.9629	3.5	34958	0.9520
0.9623	4.0	39952	0.9435
0.9509	4.5	44946	0.9442
0.9499	5.0	49940	0.9404
0.9367	5.5	54934	0.9334
0.9376	6.0	59928	0.9308
0.9247	6.5	64922	0.9270
0.9255	7.0	69916	0.9200
0.9163	7.5	74910	0.9184
0.9123	8.0	79904	0.9160
0.9071	8.5	84898	0.9147
0.9042	9.0	89892	0.9132
0.9026	9.5	94886	0.9124
0.9012	10.0	99880	0.9128

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

Downloads last month: 1

Safetensors

Model size

27.1M params

Tensor type

F32

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