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
- 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"
Upload VALIDATION.md with huggingface_hub
Browse files- VALIDATION.md +6 -6
VALIDATION.md
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# Validating marvy-14B
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This guide gives you three independent ways to confirm the fine-tune actually
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learned the ServiceNow delivery style — from a 60-second smoke test to a
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## What "working" means here
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marvy-14B is a **specialist drafting model**. A successful fine-tune should show:
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1. **Format fidelity** — it emits the delivery artifact shape on cue (user
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stories with acceptance criteria, SDD sections, test cases with
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### LM Studio (local)
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```bash
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lms load MainStack/marvy-14B
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lms server start # OpenAI-compatible on http://localhost:1234/v1
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curl -s http://localhost:1234/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "marvy-14B",
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"temperature": 0.4,
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"messages": [
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{"role": "system", "content": "You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade artifacts and favor out-of-the-box capabilities."},
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### MLX (Apple Silicon)
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```bash
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python -m mlx_lm generate --model MainStack/marvy-14B \
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--system-prompt "You are a senior ServiceNow delivery consultant..." \
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--prompt "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA." \
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--max-tokens 512 --temp 0.4
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| Invents `sys_id`s / plugin IDs | expected limitation | verify against a real instance; never trust IDs blindly |
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| marvy ppl ≈ base ppl | adapter not applied / wrong checkpoint | confirm `--adapter-path` points at the trained adapter (iter-150) |
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marvy-14B is a first-draft assistant. All output must be reviewed by a qualified
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ServiceNow consultant before client delivery or production configuration.
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# Validating marvy-1-14B
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This guide gives you three independent ways to confirm the fine-tune actually
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learned the ServiceNow delivery style — from a 60-second smoke test to a
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## What "working" means here
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+
marvy-1-14B is a **specialist drafting model**. A successful fine-tune should show:
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1. **Format fidelity** — it emits the delivery artifact shape on cue (user
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stories with acceptance criteria, SDD sections, test cases with
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### LM Studio (local)
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```bash
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lms load MainStack/marvy-1-14B
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lms server start # OpenAI-compatible on http://localhost:1234/v1
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curl -s http://localhost:1234/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "marvy-1-14B",
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"temperature": 0.4,
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"messages": [
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{"role": "system", "content": "You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade artifacts and favor out-of-the-box capabilities."},
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### MLX (Apple Silicon)
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```bash
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python -m mlx_lm generate --model MainStack/marvy-1-14B \
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--system-prompt "You are a senior ServiceNow delivery consultant..." \
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--prompt "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA." \
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--max-tokens 512 --temp 0.4
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| 134 |
| Invents `sys_id`s / plugin IDs | expected limitation | verify against a real instance; never trust IDs blindly |
|
| 135 |
| marvy ppl ≈ base ppl | adapter not applied / wrong checkpoint | confirm `--adapter-path` points at the trained adapter (iter-150) |
|
| 136 |
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+
marvy-1-14B is a first-draft assistant. All output must be reviewed by a qualified
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ServiceNow consultant before client delivery or production configuration.
|