Instructions to use AlekseyKorshuk/dalio-handwritten-io-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlekseyKorshuk/dalio-handwritten-io-1.3b with Transformers:

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

pipe = pipeline("text-generation", model="AlekseyKorshuk/dalio-handwritten-io-1.3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlekseyKorshuk/dalio-handwritten-io-1.3b")
model = AutoModelForCausalLM.from_pretrained("AlekseyKorshuk/dalio-handwritten-io-1.3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlekseyKorshuk/dalio-handwritten-io-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlekseyKorshuk/dalio-handwritten-io-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlekseyKorshuk/dalio-handwritten-io-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AlekseyKorshuk/dalio-handwritten-io-1.3b

SGLang

How to use AlekseyKorshuk/dalio-handwritten-io-1.3b 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 "AlekseyKorshuk/dalio-handwritten-io-1.3b" \
    --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": "AlekseyKorshuk/dalio-handwritten-io-1.3b",
		"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 "AlekseyKorshuk/dalio-handwritten-io-1.3b" \
        --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": "AlekseyKorshuk/dalio-handwritten-io-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlekseyKorshuk/dalio-handwritten-io-1.3b with Docker Model Runner:
```
docker model run hf.co/AlekseyKorshuk/dalio-handwritten-io-1.3b
```

dalio-handwritten-io-1.3b / README.md

AlekseyKorshuk

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metadata

license: other
tags:
  - generated_from_trainer
datasets:
  - AlekseyKorshuk/dalio-handwritten-io
metrics:
  - accuracy
model-index:
  - name: dalio-handwritten-io-1.3b
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: AlekseyKorshuk/dalio-handwritten-io
          type: AlekseyKorshuk/dalio-handwritten-io
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.06143479984145858

dalio-handwritten-io-1.3b

This model is a fine-tuned version of facebook/opt-1.3b on the AlekseyKorshuk/dalio-handwritten-io dataset. It achieves the following results on the evaluation set:

Loss: 2.3789
Accuracy: 0.0614

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: 3e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.9219	0.1	1	2.6484	0.0529
2.6938	0.2	2	2.6484	0.0529
2.6365	0.3	3	2.5508	0.0560
2.5088	0.4	4	2.5332	0.0562
2.7307	0.5	5	2.5176	0.0565
2.969	0.6	6	2.4941	0.0571
2.7283	0.7	7	2.4883	0.0567
2.6157	0.8	8	2.4766	0.0578
2.6406	0.9	9	2.4590	0.0583
2.5701	1.0	10	2.4375	0.0587
2.2017	1.1	11	2.4238	0.0587
2.0039	1.2	12	2.4219	0.0586
1.8981	1.3	13	2.4160	0.0589
1.7683	1.4	14	2.4160	0.0595
1.6746	1.5	15	2.4121	0.0600
1.8051	1.6	16	2.4102	0.0600
2.0457	1.7	17	2.4043	0.0602
1.8257	1.8	18	2.4004	0.0606
1.744	1.9	19	2.3887	0.0607
1.8232	2.0	20	2.3887	0.0607
1.4741	2.1	21	2.3828	0.0610
1.651	2.2	22	2.3770	0.0608
1.3732	2.3	23	2.3730	0.0610
1.3151	2.4	24	2.3730	0.0610
1.5302	2.5	25	2.3730	0.0610
1.2539	2.6	26	2.375	0.0612
1.6211	2.7	27	2.3770	0.0612
1.6047	2.8	28	2.3770	0.0613
1.1953	2.9	29	2.3789	0.0614
1.1621	3.0	30	2.3789	0.0614

Framework versions

Transformers 4.25.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.3.2
Tokenizers 0.12.1