Text Generation
Transformers
Safetensors
Korean
English
keural
Mixture of Experts
korean
bilingual
causal-lm
dpo
instruction-tuned
chat
preference-optimized
conversational
custom_code
Instructions to use mkd-dev/Keural-MoE-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download tokenizer_config.json from mkd-dev/Keural-MoE-14B: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
-
https://huggingface.co/mkd-dev/Keural-MoE-14B/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://mkd-dev/Keural-MoE-14B/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/mkd-dev/Keural-MoE-14B/resolve/main/tokenizer_config.json
1.15 kB
| { | |
| "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 %}" | |
| } |