---
license: creativeml-openrail-m
datasets:
- O1-OPEN/OpenO1-SFT
language:
- en
base_model: prithivMLmods/Llama-3.1-8B-Open-SFT
pipeline_tag: text-generation
library_name: transformers
tags:
- Chain-of-Thought Activation
- Llama3.1
- 8B
- CoT
- SFT
- text-generation-inference
- Ollama
- safetensors
- Question Answering
- Math
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-3.1-8B-Open-SFT-Q5_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Llama-3.1-8B-Open-SFT`](https://huggingface.co/prithivMLmods/Llama-3.1-8B-Open-SFT) 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/Llama-3.1-8B-Open-SFT) for more details on the model.

---
Model details:
-
The Llama-3.1-8B-Open-SFT model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct,
 designed for advanced text generation tasks, including conversational 
interactions, question answering, and chain-of-thought reasoning. This 
model leverages Supervised Fine-Tuning (SFT) using the O1-OPEN/OpenO1-SFT dataset to provide enhanced performance in context-sensitive and instruction-following tasks.

---
## 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/Llama-3.1-8B-Open-SFT-Q5_K_M-GGUF --hf-file llama-3.1-8b-open-sft-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-8B-Open-SFT-Q5_K_M-GGUF --hf-file llama-3.1-8b-open-sft-q5_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/Llama-3.1-8B-Open-SFT-Q5_K_M-GGUF --hf-file llama-3.1-8b-open-sft-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-8B-Open-SFT-Q5_K_M-GGUF --hf-file llama-3.1-8b-open-sft-q5_k_m.gguf -c 2048
```