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
```

qgallouedec HF Staff commited on 13 days ago

Commit

73eb084

verified ·

1 Parent(s): 3c5ba8d

Add RemoteForSequenceClassification to remote-code map

Browse files

Files changed (2) hide show

model.safetensors +1 -1
modeling_remote.py +5 -1

model.safetensors CHANGED Viewed

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modeling_remote.py CHANGED Viewed

@@ -12,10 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from transformers import LlamaForCausalLM
 from .configuration_remote import RemoteConfig
 class RemoteForCausalLM(LlamaForCausalLM):
     config_class = RemoteConfig

 # See the License for the specific language governing permissions and
 # limitations under the License.
+from transformers import LlamaForCausalLM, LlamaForSequenceClassification
 from .configuration_remote import RemoteConfig
 class RemoteForCausalLM(LlamaForCausalLM):
     config_class = RemoteConfig
+class RemoteForSequenceClassification(LlamaForSequenceClassification):
+    config_class = RemoteConfig