---
license: apache-2.0
language:
- en
base_model: prithivMLmods/Blitzar-Coder-4B-F.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- RL
- moe
- llama-cpp
- gguf-my-repo
datasets:
- livecodebench/code_generation_lite
- PrimeIntellect/verifiable-coding-problems
- likaixin/TACO-verified
- open-r1/codeforces-cots
---

# Triangle104/Blitzar-Coder-4B-F.1-Q8_0-GGUF
This model was converted to GGUF format from [`prithivMLmods/Blitzar-Coder-4B-F.1`](https://huggingface.co/prithivMLmods/Blitzar-Coder-4B-F.1) 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/Blitzar-Coder-4B-F.1) for more details on the model.

---
Blitzar-Coder-4B-F.1 is a high-efficiency, multi-language coding model fine-tuned on Qwen3-4B using larger coding traces datasets spanning 10+ programming languages including Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, and more. This model delivers exceptional code generation, debugging, and reasoning capabilities—making it an ideal tool for developers seeking advanced programming assistance under constrained compute.

---
## 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/Blitzar-Coder-4B-F.1-Q8_0-GGUF --hf-file blitzar-coder-4b-f.1-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Blitzar-Coder-4B-F.1-Q8_0-GGUF --hf-file blitzar-coder-4b-f.1-q8_0.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/Blitzar-Coder-4B-F.1-Q8_0-GGUF --hf-file blitzar-coder-4b-f.1-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Blitzar-Coder-4B-F.1-Q8_0-GGUF --hf-file blitzar-coder-4b-f.1-q8_0.gguf -c 2048
```