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 tokenizer_config.json from Montlok/DoL-1.2-Base: direct link, hf CLI and curl.
- Browser
- Download file 651 Bytes
-
https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Montlok/DoL-1.2-Base/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/tokenizer_config.json
651 Bytes
| { | |
| "add_bos_token": true, | |
| "add_eos_token": true, | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_dol.DoLTokenizer", | |
| null | |
| ] | |
| }, | |
| "bos_token": "<bos>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<eos>", | |
| "expected_manifest_sha256": "dbee445fc5573b70306ff84cdec182b798111384c9c2a463abe14574cebac737", | |
| "model_max_length": 2048, | |
| "model_vocab_size": 65536, | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "special_tokens_pattern": "bos_eos", | |
| "split_special_tokens": true, | |
| "token_type_ids_pattern": "all_zeros", | |
| "tokenizer_class": "DoLTokenizer", | |
| "truncation_side": "right", | |
| "unk_token": "<unk>" | |
| } | |