alexokita/mistral-nemo-style-step2000-GGUF overview
Mistral Nemo Style step 2000 — GGUF Fine tuned Mistral Nemo Instruct 2407 LoRA style training merged at checkpoint step 2000 , then converted to GGUF for llama…
Runs locally from ~6.96 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | alexokita/mistral-nemo-style-step2000-GGUF |
|---|---|
| Author | alexokita |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | mistralai/Mistral-Nemo-Instruct-2407 |
| Last modified | 2026-07-12T21:16:02.000Z |
Model README
---
license: apache-2.0
base_model: mistralai/Mistral-Nemo-Instruct-2407
tags:
- gguf
- mistral
- mistral-nemo
- style
- fine-tuned
- llama.cpp
- lmstudio
quantized_by: alexokita
pipeline_tag: text-generation
---
Mistral-Nemo Style (step 2000) — GGUF
Fine-tuned Mistral-Nemo-Instruct-2407 LoRA (style training) merged at checkpoint step 2000, then converted to GGUF for llama.cpp / LM Studio.
Files
| File | Quant | Size (approx) | Notes |
|------|-------|---------------|-------|
| mistral-style-step2000-q5_k_m.gguf | Q5_K_M | ~8.2 GB | Recommended quality/size |
| mistral-style-step2000-q4_k_m.gguf | Q4_K_M | ~7.0 GB | Smaller / faster |
LM Studio
- Download a
.gguffrom this repo - Import into LM Studio (or place in your models folder)
- Use the Mistral chat template if not auto-detected
Suggested sampling (starting point): temperature 1.0, top_p 0.95.
Training notes
- Base:
unsloth/mistral-nemo-instruct-2407-bnb-4bit(Mistral-Nemo-Instruct-2407) - Method: QLoRA rank 32 via Unsloth / TRL SFT
- Checkpoint: step 2000 (of 3000 configured max steps)
- Conversion: merge LoRA → HF F16 →
convert_hf_to_gguf.py→llama-quantize
License
Follow the base model license (Mistral-Nemo-Instruct-2407) and Apache-2.0 for conversion tooling.
Run alexokita/mistral-nemo-style-step2000-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models