Instructions to use Citaman/command-r-4-layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Citaman/command-r-4-layer with Transformers:

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

pipe = pipeline("text-generation", model="Citaman/command-r-4-layer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Citaman/command-r-4-layer")
model = AutoModelForCausalLM.from_pretrained("Citaman/command-r-4-layer")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Citaman/command-r-4-layer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Citaman/command-r-4-layer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Citaman/command-r-4-layer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Citaman/command-r-4-layer

SGLang

How to use Citaman/command-r-4-layer 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 "Citaman/command-r-4-layer" \
    --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": "Citaman/command-r-4-layer",
		"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 "Citaman/command-r-4-layer" \
        --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": "Citaman/command-r-4-layer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Citaman/command-r-4-layer with Docker Model Runner:
```
docker model run hf.co/Citaman/command-r-4-layer
```

command-r-4-layer / README.md

Citaman

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	---
	base_model:
	- Citaman/command-r-5-layer
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the SLERP merge method.

	### Models Merged

	The following models were included in the merge:
	* [Citaman/command-r-5-layer](https://huggingface.co/Citaman/command-r-5-layer)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	slices:
	- sources:
	- model: Citaman/command-r-5-layer
	layer_range: [0, 4]
	- model: Citaman/command-r-5-layer
	layer_range: [1, 5]
	merge_method: slerp
	base_model: Citaman/command-r-5-layer
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.5, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.5, 0.7, 0.3, 0]
	- value: 0.5
	dtype: bfloat16
	```