---
base_model: arcee-ai/Arcee-Maestro-7B-Preview
library_name: transformers
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Arcee-Maestro-7B-Preview-Q5_K_S-GGUF
This model was converted to GGUF format from [`arcee-ai/Arcee-Maestro-7B-Preview`](https://huggingface.co/arcee-ai/Arcee-Maestro-7B-Preview) 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/arcee-ai/Arcee-Maestro-7B-Preview) for more details on the model.

---
Arcee-Maestro-7B-Preview (7B) is Arcee's first reasoning model trained with reinforment learning. It is based on the Qwen2.5-7B DeepSeek-R1 distillation DeepSeek-R1-Distill-Qwen-7B with further GPRO training. Though this is just a preview of our 
upcoming work, it already shows promising improvements to mathematical 
and coding abilities across a range of tasks.

Intended Use Cases
-

Advanced reasoning

Mathematics

Coding

Training & Fine-Tuning
-



Initial Training: Began with DeepSeek-R1-Distill-Qwen-7B
GRPO:
Trained on 450,000 verified math problems
Additional bootstrapped coding examples





	
		
	

Performance
-



Arcee-Maestro-7B-Preview shows strong performance in mathematics as 
well as coding, competing against even O1 preview, a model far 
surprassing its size.



	
		
	

Limitations
-



Context Length: 128k Tokens (may vary depending on the final tokenizer settings and system resources).
Knowledge Cut-off: Training data may not reflect the latest events or developments beyond June 2024.



	
		
	

Ethical Considerations
-



Content Generation Risks: Like any language model, 
Arcee-Maestro-7B-Preview can generate potentially harmful or biased 
content if prompted in certain ways.



	
		
	

License
-



Arcee-Maestro-7B-Preview (7B) is released under the Apache-2.0 License.
 You are free to use, modify, and distribute this model in both 
commercial and non-commercial applications, subject to the terms and 
conditions of the license.

---
## 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/Arcee-Maestro-7B-Preview-Q5_K_S-GGUF --hf-file arcee-maestro-7b-preview-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Arcee-Maestro-7B-Preview-Q5_K_S-GGUF --hf-file arcee-maestro-7b-preview-q5_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/Arcee-Maestro-7B-Preview-Q5_K_S-GGUF --hf-file arcee-maestro-7b-preview-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Arcee-Maestro-7B-Preview-Q5_K_S-GGUF --hf-file arcee-maestro-7b-preview-q5_k_s.gguf -c 2048
```