Instructions to use yhavinga/gpt-neo-125M-dutch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yhavinga/gpt-neo-125M-dutch with Transformers:

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

pipe = pipeline("text-generation", model="yhavinga/gpt-neo-125M-dutch")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yhavinga/gpt-neo-125M-dutch")
model = AutoModelForMultimodalLM.from_pretrained("yhavinga/gpt-neo-125M-dutch")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yhavinga/gpt-neo-125M-dutch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yhavinga/gpt-neo-125M-dutch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yhavinga/gpt-neo-125M-dutch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yhavinga/gpt-neo-125M-dutch

SGLang

How to use yhavinga/gpt-neo-125M-dutch 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 "yhavinga/gpt-neo-125M-dutch" \
    --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": "yhavinga/gpt-neo-125M-dutch",
		"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 "yhavinga/gpt-neo-125M-dutch" \
        --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": "yhavinga/gpt-neo-125M-dutch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yhavinga/gpt-neo-125M-dutch with Docker Model Runner:
```
docker model run hf.co/yhavinga/gpt-neo-125M-dutch
```

gpt-neo-125M-dutch / flax_to_pytorch.py

yhavinga

Saving scripts, log and checkpoint at step 70000

f1818f3 over 4 years ago

raw

history blame

836 Bytes

	import torch
	import numpy as np
	import jax
	import jax.numpy as jnp
	from transformers import AutoTokenizer
	from transformers import FlaxGPTNeoForCausalLM
	from transformers import GPTNeoForCausalLM
	tokenizer = AutoTokenizer.from_pretrained(".")
	tokenizer.pad_token = tokenizer.eos_token
	model_fx = FlaxGPTNeoForCausalLM.from_pretrained(".")
	# def to_f32(t):
	# return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
	# model_fx.params = to_f32(model_fx.params)
	# model_fx.save_pretrained("./fx")
	model_pt = GPTNeoForCausalLM.from_pretrained(".", from_flax=True)
	model_pt.save_pretrained(".")
	input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
	input_ids_pt = torch.tensor(input_ids)
	logits_pt = model_pt(input_ids_pt).logits
	print(logits_pt)
	logits_fx = model_fx(input_ids).logits
	print(logits_fx)