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/morphbpe/serialization.py from Montlok/DoL-1.2-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.02 kB
-
https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/Tokenizer/morphbpe/serialization.py
- Command line
-
hf download hf://Montlok/DoL-1.2-Base/Tokenizer/morphbpe/serialization.py
-
curl -L -o serialization.py https://huggingface.co/Montlok/DoL-1.2-Base/resolve/main/Tokenizer/morphbpe/serialization.py
2.02 kB
| # -*- coding: utf-8 -*- | |
| """MorphBPE JSON v1 serialization.""" | |
| from __future__ import annotations | |
| import json | |
| from typing import Any | |
| SCHEMA_VERSION = 1 | |
| def dump(tokenizer: Any, path: str, extra_config: dict[str, Any] | None = None) -> None: | |
| """Write a MorphBPETokenizer to disk using v1 schema.""" | |
| merges_sorted = sorted(tokenizer.merges.items(), key=lambda item: item[1][1]) | |
| merges_arr = [ | |
| {"pair": [left, right], "merged": merged, "rank": rank} | |
| for (left, right), (merged, rank) in merges_sorted | |
| ] | |
| payload = { | |
| "version": SCHEMA_VERSION, | |
| "type": "morphbpe", | |
| "vocab": tokenizer.vocab, | |
| "merges": merges_arr, | |
| "special_tokens": {"unk": "<unk>"}, | |
| "config": { | |
| "boundary_constrained": True, | |
| "min_boundary_confidence": getattr( | |
| tokenizer, "min_boundary_confidence", 0.60 | |
| ), | |
| "seed_alphabet": getattr(tokenizer, "seed_alphabet", False), | |
| **(extra_config or {}), | |
| }, | |
| } | |
| with open(path, "w", encoding="utf-8") as f: | |
| json.dump(payload, f, ensure_ascii=False, indent=2) | |
| def load(path: str) -> dict[str, Any]: | |
| with open(path, "r", encoding="utf-8") as f: | |
| payload = json.load(f) | |
| version = payload.get("version") | |
| if version is not None and int(version) != SCHEMA_VERSION: | |
| raise ValueError( | |
| f"unsupported morphbpe schema version: {version}; expected {SCHEMA_VERSION}" | |
| ) | |
| return payload | |
| def merges_from_payload( | |
| payload: dict[str, Any], | |
| ) -> dict[tuple[str, str], tuple[str, int]]: | |
| merges: dict[tuple[str, str], tuple[str, int]] = {} | |
| for i, item in enumerate(payload.get("merges", [])): | |
| if isinstance(item, dict): | |
| left, right = item["pair"] | |
| merged = item.get("merged", left + right) | |
| rank = int(item.get("rank", i)) | |
| else: | |
| left, right, merged, rank = item | |
| merges[(left, right)] = (merged, rank) | |
| return merges | |