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

update model card README.md

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	---
	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
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# dalio-handwritten-io-1.3b

	This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/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