---
base_model: deepcogito/cogito-v1-preview-llama-3B
library_name: transformers
license: llama3.2
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/cogito-v1-preview-llama-3B-Q6_K-GGUF
This model was converted to GGUF format from [`deepcogito/cogito-v1-preview-llama-3B`](https://huggingface.co/deepcogito/cogito-v1-preview-llama-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/deepcogito/cogito-v1-preview-llama-3B) for more details on the model.

---
The Cogito LLMs are instruction tuned generative models (text in/text
 out). All models are released under an open license for commercial use.

-Cogito models are hybrid reasoning models. Each model can answer 
directly (standard LLM), or self-reflect before answering (like 
reasoning models).

-The LLMs are trained using Iterated Distillation and Amplification (IDA) - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.

-The models have been optimized for coding, STEM, instruction 
following and general helpfulness, and have significantly higher 
multilingual, coding and tool calling capabilities than size equivalent 
counterparts.

  --In both standard and reasoning modes, Cogito v1-preview models 
outperform their size equivalent counterparts on common industry 
benchmarks.

Each model is trained in over 30 languages and supports a context length of 128k.

---
## 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/cogito-v1-preview-llama-3B-Q6_K-GGUF --hf-file cogito-v1-preview-llama-3b-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/cogito-v1-preview-llama-3B-Q6_K-GGUF --hf-file cogito-v1-preview-llama-3b-q6_k.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/cogito-v1-preview-llama-3B-Q6_K-GGUF --hf-file cogito-v1-preview-llama-3b-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/cogito-v1-preview-llama-3B-Q6_K-GGUF --hf-file cogito-v1-preview-llama-3b-q6_k.gguf -c 2048
```