README.md

---
license: creativeml-openrail-m
language:
- en
base_model: prithivMLmods/Deepthink-Reasoning-7B
pipeline_tag: text-generation
library_name: transformers
tags:
- code-solve
- algorithm
- codepy
- qwen_base
- 7b
- CoT
- deep-think
- llama-cpp
- gguf-my-repo
---

# Triangle104/Deepthink-Reasoning-7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Deepthink-Reasoning-7B`](https://huggingface.co/prithivMLmods/Deepthink-Reasoning-7B) 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/prithivMLmods/Deepthink-Reasoning-7B) for more details on the model.

---
Model details:
-
The Deepthink-Reasoning-7B is a fine-tuned version of the Qwen2.5-7B-Instruct
 base model, designed for text generation tasks that require deep 
reasoning, logical structuring, and problem-solving. This model 
leverages its optimized architecture to provide accurate and 
contextually relevant outputs for complex queries, making it ideal for 
applications in education, programming, and creative writing. 


With its robust natural language processing capabilities, Deepthink-Reasoning-7B
 excels in generating step-by-step solutions, creative content, and 
logical analyses. Its architecture integrates advanced understanding of 
both structured and unstructured data, ensuring precise text generation 
aligned with user inputs. 


Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, 
including Chinese, English, French, Spanish, Portuguese, German, 
Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

---
## 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/Deepthink-Reasoning-7B-Q4_K_M-GGUF --hf-file deepthink-reasoning-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Deepthink-Reasoning-7B-Q4_K_M-GGUF --hf-file deepthink-reasoning-7b-q4_k_m.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/Deepthink-Reasoning-7B-Q4_K_M-GGUF --hf-file deepthink-reasoning-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Deepthink-Reasoning-7B-Q4_K_M-GGUF --hf-file deepthink-reasoning-7b-q4_k_m.gguf -c 2048
```