Instructions to use trl-internal-testing/tiny-RemoteForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trl-internal-testing/tiny-RemoteForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="trl-internal-testing/tiny-RemoteForCausalLM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-RemoteForCausalLM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use trl-internal-testing/tiny-RemoteForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trl-internal-testing/tiny-RemoteForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-RemoteForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trl-internal-testing/tiny-RemoteForCausalLM

SGLang

How to use trl-internal-testing/tiny-RemoteForCausalLM 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 "trl-internal-testing/tiny-RemoteForCausalLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-RemoteForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "trl-internal-testing/tiny-RemoteForCausalLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-RemoteForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trl-internal-testing/tiny-RemoteForCausalLM with Docker Model Runner:
```
docker model run hf.co/trl-internal-testing/tiny-RemoteForCausalLM
```

tiny-RemoteForCausalLM / modeling_remote.py

qgallouedec HF Staff

Override SeqCls __init__ to bypass AutoModel.from_config

19bab4f verified 11 days ago

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1.5 kB

	# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import torch.nn as nn
	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel

	from .configuration_remote import RemoteConfig


	class RemoteModel(LlamaModel):
	config_class = RemoteConfig


	class RemoteForCausalLM(LlamaForCausalLM):
	config_class = RemoteConfig


	class RemoteForSequenceClassification(LlamaForSequenceClassification):
	config_class = RemoteConfig

	def __init__(self, config):
	# The parent's MRO calls `AutoModel.from_config(config)`, which would re-trigger the
	# trust_remote_code prompt for `RemoteConfig`. Wire `RemoteModel` in directly instead.
	LlamaPreTrainedModel.__init__(self, config)
	self.num_labels = config.num_labels
	self.model = RemoteModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
	self.post_init()