Instructions to use rawcell/Moonlight-16B-A3B-Instruct-bruno with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rawcell/Moonlight-16B-A3B-Instruct-bruno with Transformers:

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

pipe = pipeline("text-generation", model="rawcell/Moonlight-16B-A3B-Instruct-bruno", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("rawcell/Moonlight-16B-A3B-Instruct-bruno", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("rawcell/Moonlight-16B-A3B-Instruct-bruno", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rawcell/Moonlight-16B-A3B-Instruct-bruno with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rawcell/Moonlight-16B-A3B-Instruct-bruno"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawcell/Moonlight-16B-A3B-Instruct-bruno",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rawcell/Moonlight-16B-A3B-Instruct-bruno

SGLang

How to use rawcell/Moonlight-16B-A3B-Instruct-bruno 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 "rawcell/Moonlight-16B-A3B-Instruct-bruno" \
    --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": "rawcell/Moonlight-16B-A3B-Instruct-bruno",
		"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 "rawcell/Moonlight-16B-A3B-Instruct-bruno" \
        --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": "rawcell/Moonlight-16B-A3B-Instruct-bruno",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rawcell/Moonlight-16B-A3B-Instruct-bruno with Docker Model Runner:
```
docker model run hf.co/rawcell/Moonlight-16B-A3B-Instruct-bruno
```

rawcell commited on Feb 4

Commit

3b70871

verified ·

1 Parent(s): 4fdc85b

Upload tokenizer_config.json with huggingface_hub

Browse files

Files changed (1) hide show

tokenizer_config.json +4 -1

tokenizer_config.json CHANGED Viewed

@@ -81,7 +81,10 @@
     "<|im_middle|>"
   ],
   "auto_map": {
-    "AutoTokenizer": "tokenization_moonshot.MoonshotTokenizer"
   },
   "bos_token": "[BOS]",
   "chat_template": "{%- for message in messages -%}{%- if loop.first and messages[0]['role'] != 'system' -%}<|im_system|>system<|im_middle|>You are a helpful assistant<|im_end|>{%- endif -%}{%- if message['role'] == 'system' -%}<|im_system|>{%- endif -%}{%- if message['role'] == 'user' -%}<|im_user|>{%- endif -%}{%- if message['role'] == 'assistant' -%}<|im_assistant|>{%- endif -%}{{ message['role'] }}<|im_middle|>{{message['content']}}<|im_end|>{%- endfor -%}{%- if add_generation_prompt -%}<|im_assistant|>assistant<|im_middle|>{%- endif -%}",

     "<|im_middle|>"
   ],
   "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_moonshot.TikTokenTokenizer",
+      null
+    ]
   },
   "bos_token": "[BOS]",
   "chat_template": "{%- for message in messages -%}{%- if loop.first and messages[0]['role'] != 'system' -%}<|im_system|>system<|im_middle|>You are a helpful assistant<|im_end|>{%- endif -%}{%- if message['role'] == 'system' -%}<|im_system|>{%- endif -%}{%- if message['role'] == 'user' -%}<|im_user|>{%- endif -%}{%- if message['role'] == 'assistant' -%}<|im_assistant|>{%- endif -%}{{ message['role'] }}<|im_middle|>{{message['content']}}<|im_end|>{%- endfor -%}{%- if add_generation_prompt -%}<|im_assistant|>assistant<|im_middle|>{%- endif -%}",