Instructions to use eugenepentland/Minotaur-13b-Landmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eugenepentland/Minotaur-13b-Landmark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="eugenepentland/Minotaur-13b-Landmark", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("eugenepentland/Minotaur-13b-Landmark", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("eugenepentland/Minotaur-13b-Landmark", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use eugenepentland/Minotaur-13b-Landmark with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eugenepentland/Minotaur-13b-Landmark" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "eugenepentland/Minotaur-13b-Landmark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/eugenepentland/Minotaur-13b-Landmark
- SGLang
How to use eugenepentland/Minotaur-13b-Landmark 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 "eugenepentland/Minotaur-13b-Landmark" \ --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": "eugenepentland/Minotaur-13b-Landmark", "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 "eugenepentland/Minotaur-13b-Landmark" \ --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": "eugenepentland/Minotaur-13b-Landmark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use eugenepentland/Minotaur-13b-Landmark with Docker Model Runner:
docker model run hf.co/eugenepentland/Minotaur-13b-Landmark
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6d3e318 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LLaMA model configuration"""
from transformers import LlamaConfig as HFLlamaConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class LlamaConfig(HFLlamaConfig):
model_type = "llama"
def __init__(
self,
mem_id=32001,
mem_freq=50,
mem_top_k=5,
mem_max_seq_len=255,
mem_max_cache_size=None,
**kwargs,
):
self.mem_id = mem_id
self.mem_freq = mem_freq
self.mem_top_k = mem_top_k
self.mem_max_seq_len = mem_max_seq_len
self.mem_max_cache_size = mem_max_cache_size
super().__init__(**kwargs)
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