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

Libraries

How to use floriangardin/chord_model with Transformers:

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

pipe = pipeline("text-generation", model="floriangardin/chord_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("floriangardin/chord_model")
model = AutoModelForCausalLM.from_pretrained("floriangardin/chord_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use floriangardin/chord_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/floriangardin/chord_model

SGLang

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

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

chord_model

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4598

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 444
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.3
training_steps: 0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.2264	0.11	500	1.1269
0.9624	0.21	1000	0.9066
0.8598	0.32	1500	0.8128
0.8209	0.43	2000	0.7626
0.7483	0.53	2500	0.7272
0.7391	0.64	3000	0.7032
0.7052	0.75	3500	0.6739
0.6998	0.86	4000	0.6503
0.6901	0.96	4500	0.6244
0.6348	1.07	5000	0.6100
0.654	1.18	5500	0.5891
0.6227	1.28	6000	0.5765
0.6148	1.39	6500	0.5624
0.5973	1.5	7000	0.5538
0.5853	1.6	7500	0.5441
0.56	1.71	8000	0.5407
0.574	1.82	8500	0.5342
0.5589	1.92	9000	0.5296
0.5634	2.03	9500	0.5254
0.543	2.14	10000	0.5208
0.5792	2.25	10500	0.5159
0.5571	2.35	11000	0.5064
0.5408	2.46	11500	0.4957
0.5398	2.57	12000	0.4882
0.537	2.67	12500	0.4834
0.5512	2.78	13000	0.4786
0.4842	2.89	13500	0.4753
0.5275	2.99	14000	0.4721
0.4899	3.1	14500	0.4710
0.5222	3.21	15000	0.4666
0.4929	3.31	15500	0.4645
0.5049	3.42	16000	0.4631
0.5002	3.53	16500	0.4613
0.505	3.64	17000	0.4611
0.507	3.74	17500	0.4602
0.5169	3.85	18000	0.4598
0.501	3.96	18500	0.4598

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Downloads last month: 5

Safetensors

Model size

5.59M params

Tensor type

F32