README.md

---
library_name: transformers
tags:
- reasoning
- thinking
- cot
- deepseek
- Llama 3.2
- 128k context
- fine tune
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III
license: llama3.2
---

# Triangle104/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III-Q5_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III`](https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III) for more details on the model.

---


This a "Class 1" (settings will enhance operation) model:


For all settings used for this model (including specifics for its 
"class"), including example generation(s) and for advanced settings 
guide (which many times addresses any model issue(s)), including methods
 to improve model performance for all use case(s) as well as chat, 
roleplay and other use case(s) (especially for use case(s) beyond the 
model's design) please see:


[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]


REASON:


Regardless of "model class" this document will detail methods to enhance operations.


If the model is a Class 3/4 model the default settings (parameters, 
samplers, advanced samplers) must be set for "use case(s)" uses 
correctly. Some AI/LLM apps DO NOT have consistant default setting(s) 
which result in sub-par model operation. Like wise for Class 3/4 models 
(which operate somewhat to very differently than standard models) 
additional samplers and advanced samplers settings are required to 
"smooth out" operation, AND/OR also allow full operation for use cases 
the model was not designed for.


BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):


This document also details parameters, sampler and advanced samplers 
that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of 
course source code operation too - to enhance the operation of any 
model.


[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]


NOTE:


I strongly suggest you also visit the DavidAU GGUF (below) repo too 
for more details in using this model ; especially if it is "Class 3" or 
"Class 4" to get maximum performance from the model.

---

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III-Q5_K_S-GGUF --hf-file deep-reasoning-llama-3.2-jametmini-3b-mk.iii-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III-Q5_K_S-GGUF --hf-file deep-reasoning-llama-3.2-jametmini-3b-mk.iii-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III-Q5_K_S-GGUF --hf-file deep-reasoning-llama-3.2-jametmini-3b-mk.iii-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-JametMini-3B-MK.III-Q5_K_S-GGUF --hf-file deep-reasoning-llama-3.2-jametmini-3b-mk.iii-q5_k_s.gguf -c 2048
```