Text Generation
Transformers
English
zenith
tenstorrent
code
reasoning
Mixture of Experts
ring-attention
eq-adapter
matrix-corp
Instructions to use Matrix-Corp/Zenith-7b-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matrix-Corp/Zenith-7b-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Matrix-Corp/Zenith-7b-V1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Matrix-Corp/Zenith-7b-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Matrix-Corp/Zenith-7b-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Matrix-Corp/Zenith-7b-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Matrix-Corp/Zenith-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Matrix-Corp/Zenith-7b-V1
- SGLang
How to use Matrix-Corp/Zenith-7b-V1 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 "Matrix-Corp/Zenith-7b-V1" \ --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": "Matrix-Corp/Zenith-7b-V1", "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 "Matrix-Corp/Zenith-7b-V1" \ --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": "Matrix-Corp/Zenith-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Matrix-Corp/Zenith-7b-V1 with Docker Model Runner:
docker model run hf.co/Matrix-Corp/Zenith-7b-V1
| """Ultra-sophisticated data pipeline for OpenThoughts-1.2M and custom datasets.""" | |
| from .openthoughts_processor import OpenThoughtsProcessor, OpenThoughtsDataset | |
| from .advanced_tokenizer import AdvancedTokenizer, TokenizerManager | |
| from .quality_filter import QualityFilter, filter_dataset | |
| from .curriculum_sampler import CurriculumSampler, DifficultyAwareSampler | |
| from .data_augmentation import DataAugmenter, augment_sample | |
| from .preprocessing import preprocess_conversation, extract_thoughts, format_for_training | |
| from .utils import compute_length_statistics, analyze_dataset_quality | |
| __all__ = [ | |
| "OpenThoughtsProcessor", | |
| "OpenThoughtsDataset", | |
| "AdvancedTokenizer", | |
| "TokenizerManager", | |
| "QualityFilter", | |
| "filter_dataset", | |
| "CurriculumSampler", | |
| "DifficultyAwareSampler", | |
| "DataAugmenter", | |
| "augment_sample", | |
| "preprocess_conversation", | |
| "extract_thoughts", | |
| "format_for_training", | |
| "compute_length_statistics", | |
| "analyze_dataset_quality", | |
| ] | |