Instructions to use fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407 with Transformers:

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

pipe = pipeline("text-generation", model="fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407")
model = AutoModelForCausalLM.from_pretrained("fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407

SGLang

How to use fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407 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 "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407" \
    --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": "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407",
		"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 "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407" \
        --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": "fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407 with Docker Model Runner:
```
docker model run hf.co/fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407
```

fpadovani commited on Jun 19

Commit

a909263

verified ·

1 Parent(s): 1b375bf

Training in progress, step 500

Browse files

Files changed (8) hide show

README.md +58 -0
added_tokens.json +1768 -0
config.json +34 -0
model.safetensors +3 -0
special_tokens_map.json +1815 -0
spiece.model +3 -0
tokenizer_config.json +0 -0
training_args.bin +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,58 @@

+---
+base_model: goldfish-models/urd_arab_100mb
+library_name: transformers
+model_name: urd-arab-100mb-ppt-Dp-10mb_seed3407
+tags:
+- generated_from_trainer
+- sft
+- trl
+licence: license
+---
+# Model Card for urd-arab-100mb-ppt-Dp-10mb_seed3407
+This model is a fine-tuned version of [goldfish-models/urd_arab_100mb](https://huggingface.co/goldfish-models/urd_arab_100mb).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+## Quick start
+```python
+from transformers import pipeline
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="fpadovani/urd-arab-100mb-ppt-Dp-10mb_seed3407", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/f-padovani-university-of-groningen/new_tokenizers/runs/5poyuc8g)
+This model was trained with SFT.
+### Framework versions
+- TRL: 0.23.0
+- Transformers: 4.56.2
+- Pytorch: 2.11.0
+- Datasets: 4.8.4
+- Tokenizers: 0.22.1
+## Citations
+Cite TRL as:
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

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