Text Generation
Transformers
Safetensors
Khasi
English
Garo
gemma2
khasi
northeast-india
low-resource
continued-pretraining
instruction-tuning
bilingual
Garo
Meghalaya
text-generation-inference
Instructions to use MWirelabs/Kren-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MWirelabs/Kren-M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MWirelabs/Kren-M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MWirelabs/Kren-M") model = AutoModelForCausalLM.from_pretrained("MWirelabs/Kren-M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MWirelabs/Kren-M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MWirelabs/Kren-M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MWirelabs/Kren-M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MWirelabs/Kren-M
- SGLang
How to use MWirelabs/Kren-M 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 "MWirelabs/Kren-M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MWirelabs/Kren-M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "MWirelabs/Kren-M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MWirelabs/Kren-M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MWirelabs/Kren-M with Docker Model Runner:
docker model run hf.co/MWirelabs/Kren-M
Request for using MWirelabs/Kren-m access.
#4
by kranthi6375 - opened
Hello Team,
I am following up on my request for access to MWirelabs/Kren-m. I raised the access request on 21 July, but I have not yet received the required access.
Could you please check the status of my request and let me know if any additional information or action is needed from my side? I would appreciate it if the access could be granted at the earliest, as it is required for my work.
Thank you for your assistance.
Kind regards,
[Kranthi Reddy]