Instructions to use Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch with Transformers:

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

pipe = pipeline("text-generation", model="Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch")
model = AutoModelForCausalLM.from_pretrained("Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch

SGLang

How to use Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch 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 "Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch" \
    --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": "Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch",
		"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 "Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch" \
        --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": "Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch with Docker Model Runner:
```
docker model run hf.co/Ignaciohhhhggfgjfrffd/multi-dataset-model-from-scratch
```

multi-dataset-model-from-scratch / tokenizer.json

Ignaciohhhhggfgjfrffd

Upload full model and tokenizer trained from scratch

bcf502d verified 12 months ago

History Blame Contribute Delete

49.8 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3db9f4732feae5362dfcd1544ebfaf27e2b9022b4c4efd08d2c532d65a0395c6
Size of remote file:: 49.8 MB
SHA256:: a11cbf577f7fa6ab99247e2a6b919fe9d7fe79dd171998154948dbc9a5001cbd

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.