Instructions to use mkd-dev/Keural-MoE-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkd-dev/Keural-MoE-14B with Transformers:

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

pipe = pipeline("text-generation", model="mkd-dev/Keural-MoE-14B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mkd-dev/Keural-MoE-14B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mkd-dev/Keural-MoE-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mkd-dev/Keural-MoE-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkd-dev/Keural-MoE-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mkd-dev/Keural-MoE-14B

SGLang

How to use mkd-dev/Keural-MoE-14B 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 "mkd-dev/Keural-MoE-14B" \
    --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": "mkd-dev/Keural-MoE-14B",
		"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 "mkd-dev/Keural-MoE-14B" \
        --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": "mkd-dev/Keural-MoE-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mkd-dev/Keural-MoE-14B with Docker Model Runner:
```
docker model run hf.co/mkd-dev/Keural-MoE-14B
```

Keural-MoE-14B

File size: 1,153 Bytes

264c832

{
  "bos_token": "<bos>",
  "eos_token": "<|im_end|>",
  "unk_token": "<unk>",
  "pad_token": "<pad>",
  "model_max_length": 4096,
  "tokenizer_class": "LlamaTokenizer",
  "sp_model_kwargs": {},
  "legacy": false,
  "added_tokens_decoder": {
    "0":      {"content": "<pad>",        "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true},
    "1":      {"content": "<bos>",        "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true},
    "2":      {"content": "<eos>",        "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true},
    "131072": {"content": "<|im_start|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true},
    "131073": {"content": "<|im_end|>",   "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true}
  },
  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
}