Text Generation
Transformers
Safetensors
Mongolian
rdt
traditional-mongolian
recurrent-depth-transformer
mamba
causal-lm
custom-code
custom_code
Instructions to use Montlok/DoL-1.2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Montlok/DoL-1.2-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Montlok/DoL-1.2-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Montlok/DoL-1.2-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Montlok/DoL-1.2-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Montlok/DoL-1.2-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Montlok/DoL-1.2-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Montlok/DoL-1.2-Base
- SGLang
How to use Montlok/DoL-1.2-Base 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 "Montlok/DoL-1.2-Base" \ --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": "Montlok/DoL-1.2-Base", "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 "Montlok/DoL-1.2-Base" \ --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": "Montlok/DoL-1.2-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Montlok/DoL-1.2-Base with Docker Model Runner:
docker model run hf.co/Montlok/DoL-1.2-Base
Download Model/layers/__init__.py from Montlok/DoL-1.2-Base: direct link, hf CLI and curl.
- Browser
- Download file 698 Bytes
-
https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/Model/layers/__init__.py
- Command line
-
hf download hf://Montlok/DoL-1.2-Base/Model/layers/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/Model/layers/__init__.py
698 Bytes
| # -*- coding: utf-8 -*- | |
| """Core neural network layers used by the RDT model.""" | |
| from Model.layers.mamba3_layer import Mamba3Layer, NaiveSSM, official_available | |
| from Model.layers.mixture_lora_ffn import MixtureLoRAFFN | |
| from Model.layers.mla import MLA | |
| from Model.layers.rmsnorm import GroupedRMSNorm, RMSNorm | |
| from Model.layers.rope import ( | |
| MorphologicalRoPE, | |
| apply_rope, | |
| derive_morph_info_from_offsets, | |
| ) | |
| from Model.layers.swiglu import SwiGLU | |
| __all__ = [ | |
| "GroupedRMSNorm", | |
| "MLA", | |
| "Mamba3Layer", | |
| "MixtureLoRAFFN", | |
| "MorphologicalRoPE", | |
| "NaiveSSM", | |
| "RMSNorm", | |
| "SwiGLU", | |
| "apply_rope", | |
| "derive_morph_info_from_offsets", | |
| "official_available", | |
| ] | |