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-Instruct-uncensored-3B
---

# Triangle104/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B-Q4_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B`](https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B) 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-Instruct-uncensored-3B) for more details on the model.

---
This model was fined tuned by DavidAU with "reasoning"/"thinking" from this model:


[ https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored ]


Please give this model maker a shout out for work well done.


Reasoning/Thinking Activation:


Make sure you see the MOE Model project above for detailed operation instructions.


If you DO NOT set a system prompt/role, the model will still reason in most cases - but generally in text only.


For prompts that do not strictly imply "reasoning/thought" the model MAY simply process the prompt.


However, with a system prompt ("Suggested" or "Advanced") set the model will ALWAYS "reason" / "think".


If you set / have a system prompt this will affect both "generation" and "thinking/reasoning".


SIMPLE:


This is the generic system prompt used for generation and testing: 


You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.




This System Role/Prompt may give you a lot more "creative results": 


Use vivid and graphic words focusing on verbs and use current 2020 fiction writing style. Use metaphor(s) that fit the context of the situation (and reveal character) rather than similes."




SUGGESTED:


You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside   tags, and then provide your solution or response to the problem.  




ADVANCED:


Logical and Creative - these will SIGNFICANTLY alter the output, and many times improve it too.


This will also cause more thoughts, deeper thoughts, and in many cases more detailed/stronger thoughts too.


Keep in mind you may also want to test the model with NO system prompt at all - including the default one.


Special Credit to: Eric Hartford, Cognitivecomputations ; these are based on his work.


CRITICAL: 


Copy and paste exactly as shown, preserve formatting and line breaks.


SIDE NOTE: 


These can be used in ANY Deepseek / Thinking model, including models not at this repo. 


These, if used in a "non thinking" model, will also alter model performance too.


You are an AI assistant developed by the world wide community of ai experts.

Your primary directive is to provide well-reasoned, structured, and extensively detailed responses.

Formatting Requirements:

1. Always structure your replies using: {reasoning}{answer}
2. The  block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the  block may be left empty.
4. The user does not see the  section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a  and proceed to the {answer}

Response Guidelines:

1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Scientific and Logical Approach: Your explanations should reflect the depth and precision of the greatest scientific minds.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.




CREATIVE:


You are an AI assistant developed by a world wide community of ai experts.

Your primary directive is to provide highly creative, well-reasoned, structured, and extensively detailed responses.

Formatting Requirements:

1. Always structure your replies using: {reasoning}{answer}
2. The  block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the  block may be left empty.
4. The user does not see the  section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a  and proceed to the {answer}

Response Guidelines:

1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Creative and Logical Approach: Your explanations should reflect the depth and precision of the greatest creative minds first.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.





IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers


If you are going to use this model, (source, GGUF or a different 
quant), please review this document for critical parameter, sampler and 
advance sampler settings (for multiple AI/LLM aps).


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 ]

---
## 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-Instruct-uncensored-3B-Q4_K_S-GGUF --hf-file deep-reasoning-llama-3.2-instruct-uncensored-3b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B-Q4_K_S-GGUF --hf-file deep-reasoning-llama-3.2-instruct-uncensored-3b-q4_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-Instruct-uncensored-3B-Q4_K_S-GGUF --hf-file deep-reasoning-llama-3.2-instruct-uncensored-3b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B-Q4_K_S-GGUF --hf-file deep-reasoning-llama-3.2-instruct-uncensored-3b-q4_k_s.gguf -c 2048
```