| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - mistralai/Mistral-Nemo-12B-Base |
| tags: |
| - text-generation |
| - chat |
| - instruction-following |
| - reasoning |
| - coding |
| --- |
| |
| # Mistral-NeMo-12B-Instruct |
|
|
| Mistral-NeMo-12B-Instruct is an instruction-optimized large language model built on top of the Mistral-NeMo-12B foundation. It is tailored for dialogue systems, structured task execution, and complex reasoning scenarios. The model emphasizes clarity, stability across multi-turn interactions, and high-quality output generation for both technical and general-purpose tasks. |
|
|
| For streamlined local deployment, GGUF quantized builds (including Q4_K_M and Q5_K_M) are commonly used to reduce memory demands and improve inference performance on consumer hardware. |
|
|
| --- |
|
|
| ## Overview |
|
|
| - **Model name:** Mistral-NeMo-12B-Instruct |
| - **Base architecture:** Decoder-only transformer |
| - **Developer:** NVIDIA & Mistral AI |
| - **License:** Apache-2.0 |
| - **Quantized Versions:** |
| - Q4_K_M (4-bit) |
| - Q5_K_M (5-bit) |
| - **Parameter count:** ~12B |
| - **Tokenizer:** NeMo-aligned tokenizer |
| - **Model type:** Instruction-tuned language model |
|
|
| --- |
|
|
| ## Quantization Details |
|
|
| ### Q4_K_M |
|
|
| - Approx. ~ 70% size reduction (~ 6.96) |
| - Reduced VRAM/RAM requirements |
| - Suitable for CPU-heavy or limited-GPU setups |
| - Faster inference speed |
| - Slight quality trade-off in edge cases |
|
|
| ### Q5_K_M |
|
|
| - Approx. ~ 64% size reduction (~ 8.13) |
| - Balanced compression and quality |
| - Improved stability over 4-bit variants |
| - Better retention of reasoning depth |
| - Preferred when additional memory is available |
|
|
| --- |
|
|
| ## Key Features |
|
|
| - **Instruction alignment** |
| Tuned to respond accurately to detailed prompts. |
|
|
| - **Dialogue consistency** |
| Maintains structured context across extended exchanges. |
|
|
| - **Problem-solving capability** |
| Handles analytical and multi-step reasoning tasks. |
|
|
| - **Programming support** |
| Assists with code writing, refactoring, and explanations. |
|
|
| - **Formatted output generation** |
| Capable of structured responses when required. |
|
|
| - **Extended context handling** |
| Designed to manage long prompts efficiently. |
|
|
| --- |
|
|
| ## Training Details |
|
|
| This model originates from the Mistral-NeMo-12B base and undergoes additional refinement to enhance responsiveness and alignment with user instructions. |
|
|
| - Pretraining: Large-scale autoregressive training on diverse multilingual and code datasets. |
| - Architecture: 12B parameter transformer decoder model. |
| - Instruction Tuning: Supervised fine-tuning for improved instruction following and chat performance. |
| - Context Length: Supports up to 128K token context window. |
|
|
| --- |
|
|
| ## Usage |
|
|
| ### llama.cpp |
|
|
| ```bash |
| ./llama-cli \ |
| -m SandlogicTechnologies\Mistral-NeMo-12B-Instruct_Q4_K_M.gguf \ |
| -p "Summarize the key differences between CNNs and Transformers." |
| ``` |
|
|
| --- |
|
|
| ## Recommended Use Cases |
|
|
| - **Conversational systems** |
| Multi-turn assistant-style interactions. |
|
|
| - **Technical documentation support** |
| Drafting and summarizing structured material. |
|
|
| - **Code-related workflows** |
| Generating and reviewing source code. |
|
|
| - **Analytical reasoning tasks** |
| Assisting with structured logic and problem solving. |
|
|
| - **Automation pipelines** |
| Producing formatted outputs for downstream systems. |
|
|
| --- |
|
|
|
|
| ## Acknowledgments |
|
|
| These quantized models are based on the original work by **mistralai** development team. |
|
|
| Special thanks to: |
| - The [mistralai](https://huggingface.co/mistralai) team for developing and releasing the [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) model. |
|
|
| - **Georgi Gerganov** and the entire [`llama.cpp`](https://github.com/ggerganov/llama.cpp) open-source community for enabling efficient model quantization and inference via the GGUF format. |
|
|
| --- |
|
|
|
|
| ## Contact |
| For any inquiries or support, please contact us at support@sandlogic.com or visit our [Website](https://www.sandlogic.com/). |
|
|
