Instructions to use Anurich/Jeeves-Small-75M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anurich/Jeeves-Small-75M with Transformers:

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

pipe = pipeline("text-generation", model="Anurich/Jeeves-Small-75M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Anurich/Jeeves-Small-75M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anurich/Jeeves-Small-75M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anurich/Jeeves-Small-75M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anurich/Jeeves-Small-75M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anurich/Jeeves-Small-75M

SGLang

How to use Anurich/Jeeves-Small-75M 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 "Anurich/Jeeves-Small-75M" \
    --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": "Anurich/Jeeves-Small-75M",
		"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 "Anurich/Jeeves-Small-75M" \
        --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": "Anurich/Jeeves-Small-75M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anurich/Jeeves-Small-75M with Docker Model Runner:
```
docker model run hf.co/Anurich/Jeeves-Small-75M
```

Anurich commited on Feb 21

Commit

ddffa29

verified ·

1 Parent(s): ca73a01

Update configuration_jeeves.py

Browse files

Files changed (1) hide show

configuration_jeeves.py +5 -0

configuration_jeeves.py CHANGED Viewed

@@ -88,6 +88,11 @@ class JeevesConfig(PretrainedConfig):
         self.head_dim = d_model // n_heads
         self.hidden_size = d_model  # HF convention
     @property
     def effective_depth(self) -> int:
         if self.loop_block_idx is not None:

         self.head_dim = d_model // n_heads
         self.hidden_size = d_model  # HF convention
+        # Standard HF aliases (required by GenerationMixin in transformers 5.x)
+        self.num_hidden_layers = n_layers
+        self.num_attention_heads = n_heads
+        self.num_key_value_heads = n_kv_heads
     @property
     def effective_depth(self) -> int:
         if self.loop_block_idx is not None: