---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
tags:
- nvidia
- AceInstruct
- code
- math
- general_domain
- instruct_model
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/AceInstruct-7B
---

# Triangle104/AceInstruct-7B-Q4_K_S-GGUF
This model was converted to GGUF format from [`nvidia/AceInstruct-7B`](https://huggingface.co/nvidia/AceInstruct-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/nvidia/AceInstruct-7B) for more details on the model.

---
We introduce AceInstruct, a family of advanced SFT models for coding,
 mathematics, and general-purpose tasks. The AceInstruct family, which 
includes AceInstruct-1.5B, 7B, and 72B, is Improved using Qwen.
These models are fine-tuned on Qwen2.5-Base using general SFT datasets. These same datasets are also used in the training of AceMath-Instruct.
 Different from AceMath-Instruct which is specialized for math 
questions, AceInstruct is versatile and can be applied to a wide range 
of domains. Benchmark evaluations across coding, mathematics, and 
general knowledge tasks demonstrate that AceInstruct delivers 
performance comparable to Qwen2.5-Instruct.


For more information about AceInstruct, check our website and paper.

---
## 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/AceInstruct-7B-Q4_K_S-GGUF --hf-file aceinstruct-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/AceInstruct-7B-Q4_K_S-GGUF --hf-file aceinstruct-7b-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/AceInstruct-7B-Q4_K_S-GGUF --hf-file aceinstruct-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/AceInstruct-7B-Q4_K_S-GGUF --hf-file aceinstruct-7b-q4_k_s.gguf -c 2048
```