Instructions to use joel-crasto/TEXT-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joel-crasto/TEXT-01 with Transformers:

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

pipe = pipeline("text-generation", model="joel-crasto/TEXT-01")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joel-crasto/TEXT-01")
model = AutoModelForCausalLM.from_pretrained("joel-crasto/TEXT-01", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use joel-crasto/TEXT-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joel-crasto/TEXT-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joel-crasto/TEXT-01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/joel-crasto/TEXT-01

SGLang

How to use joel-crasto/TEXT-01 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 "joel-crasto/TEXT-01" \
    --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": "joel-crasto/TEXT-01",
		"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 "joel-crasto/TEXT-01" \
        --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": "joel-crasto/TEXT-01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use joel-crasto/TEXT-01 with Docker Model Runner:
```
docker model run hf.co/joel-crasto/TEXT-01
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FACT-1

This model is based on the GPT-2 model shape, trained from scratch on the wikimedia/wikipedia dataset. It achieves the following results on the evaluation set:

eval_loss: 2.0494
eval_runtime: 443.6756
eval_samples_per_second: 225.39
eval_steps_per_second: 28.174
epoch: 4.2665
step: 60000

Model description

It is purely an experimental model for learning purposes, and due to the small dataset and train time, it may function as autocomplete with more tuning, but in its current state does not produce coherent sentences.

Intended uses & limitations

Not intended for use.

Sample prompt:

generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
print(generator("Planes are designed", 
    max_length=100,
    do_sample=True,
    top_p=0.9,    
    temperature=0.8,   
    repetition_penalty=1.2, 
    no_repeat_ngram_size=2 ))

Sample response:

[{'generated_text': 'Planes are designed to to be\n1., for in-Aoonoono...'}]

Training and evaluation data

Subset of wikimedia/wikipedia dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
training_steps: 100000
mixed_precision_training: Native AMP

Framework versions

Transformers 4.56.0
Pytorch 2.5.1+cu121
Datasets 4.0.0
Tokenizers 0.22.0

Downloads last month: 7

Safetensors

Model size

51.2M params

Tensor type

F32

Model tree for joel-crasto/TEXT-01

Quantizations

1 model