README.md

---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- esper
- esper-3
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-8b
- 8b
- reasoning
- code
- code-instruct
- python
- javascript
- dev-ops
- jenkins
- terraform
- scripting
- powershell
- azure
- aws
- gcp
- cloud
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
- llama-cpp
- gguf-my-repo
base_model: ValiantLabs/Qwen3-8B-Esper3
datasets:
- sequelbox/Titanium2.1-DeepSeek-R1
- sequelbox/Tachibana2-DeepSeek-R1
- sequelbox/Raiden-DeepSeek-R1
license: apache-2.0
---

# Triangle104/Qwen3-8B-Esper3-Q5_K_S-GGUF
This model was converted to GGUF format from [`ValiantLabs/Qwen3-8B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3) 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/ValiantLabs/Qwen3-8B-Esper3) for more details on the model.

---
Esper 3 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3.

- Finetuned on our DevOps and architecture reasoning and code reasoning data generated with Deepseek R1!
- Improved general and creative reasoning to supplement problem-solving and general chat performance.
- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!

---
## 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/Qwen3-8B-Esper3-Q5_K_S-GGUF --hf-file qwen3-8b-esper3-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-8B-Esper3-Q5_K_S-GGUF --hf-file qwen3-8b-esper3-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/Qwen3-8B-Esper3-Q5_K_S-GGUF --hf-file qwen3-8b-esper3-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Qwen3-8B-Esper3-Q5_K_S-GGUF --hf-file qwen3-8b-esper3-q5_k_s.gguf -c 2048
```