Instructions to use MainStack/marvy-1-14B-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MainStack/marvy-1-14B-lora with PEFT:
Task type is invalid.
- MLX
How to use MainStack/marvy-1-14B-lora with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("MainStack/marvy-1-14B-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use MainStack/marvy-1-14B-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "MainStack/marvy-1-14B-lora" --prompt "Once upon a time"
| marvy-1-14B | |
| Copyright 2026 MainStack | |
| This product is licensed under the Apache License, Version 2.0 (the "License"). | |
| You may obtain a copy of the License in the accompanying LICENSE file or at: | |
| http://www.apache.org/licenses/LICENSE-2.0 | |
| ================================================================================ | |
| Attribution request (downstream use) | |
| ================================================================================ | |
| marvy-1-14B was created by MainStack (https://huggingface.co/MainStack). | |
| If you use marvy-1-14B as a baseline, fine-tune it, distill from it, evaluate | |
| against it, or otherwise build on it, please credit MainStack and link to: | |
| https://huggingface.co/MainStack/marvy-1-14B | |
| Under the Apache License, Version 2.0, this NOTICE file MUST be retained and | |
| reproduced in any derivative works and redistributions (License §4(d)). | |
| ================================================================================ | |
| Dual licensing | |
| ================================================================================ | |
| * Model weights (safetensors / GGUF / LoRA adapter): Apache-2.0 (LICENSE). | |
| * MainStack original contributions — model cards, documentation, benchmark, | |
| charts, and curated training methodology: CC-BY-4.0 (LICENSE-CC-BY-4.0). | |
| Reuse of MainStack's contributions requires attribution to MainStack under the | |
| terms of CC-BY-4.0. See LICENSING.md for the full breakdown. | |
| ================================================================================ | |
| Attribution | |
| ================================================================================ | |
| marvy-1-14B is a fine-tuned derivative of: | |
| Qwen2.5-14B-Instruct | |
| Copyright Alibaba Cloud / Qwen Team | |
| Licensed under the Apache License, Version 2.0 | |
| https://huggingface.co/Qwen/Qwen2.5-14B-Instruct | |
| The base model weights are the property of their respective authors and are | |
| used and redistributed in modified (fine-tuned) form under the terms of the | |
| Apache License, Version 2.0. | |
| Citation for the base model: | |
| @misc{qwen2.5, | |
| title = {Qwen2.5: A Party of Foundation Models}, | |
| author = {Qwen Team}, | |
| year = {2024}, | |
| url = {https://qwenlm.github.io/blog/qwen2.5/} | |
| } | |
| @article{qwen2, | |
| title = {Qwen2 Technical Report}, | |
| author = {Qwen Team}, | |
| journal= {arXiv preprint arXiv:2407.10671}, | |
| year = {2024} | |
| } | |
| ================================================================================ | |
| Tooling | |
| ================================================================================ | |
| Trained and fused with MLX-LM (https://github.com/ml-explore/mlx-lm), | |
| Copyright Apple Inc., licensed under the MIT License. | |
| ================================================================================ | |
| Training data provenance | |
| ================================================================================ | |
| marvy-1-14B was fine-tuned on a corpus of anonymized ServiceNow delivery | |
| artifacts. All customer and partner names were replaced with stable aliases, | |
| and emails, hostnames, IP addresses, and credential-bearing files were removed | |
| or redacted prior to training. No customer-identifying information is present | |
| in the training corpus. See the model card for the full redaction methodology. | |