README.md

---
license: other
language:
- en
tags:
- causal-lm
- code
- llama-cpp
- gguf-my-repo
metrics:
- code_eval
library_name: transformers
base_model: stabilityai/stable-code-instruct-3b
model-index:
- name: stabilityai/stable-code-instruct-3b
  results:
  - task:
      type: text-generation
    dataset:
      name: MultiPL-HumanEval (Python)
      type: nuprl/MultiPL-E
    metrics:
    - type: pass@1
      value: 32.4
      name: pass@1
      verified: false
    - type: pass@1
      value: 30.9
      name: pass@1
      verified: false
    - type: pass@1
      value: 32.1
      name: pass@1
      verified: false
    - type: pass@1
      value: 32.1
      name: pass@1
      verified: false
    - type: pass@1
      value: 24.2
      name: pass@1
      verified: false
    - type: pass@1
      value: 23.0
      name: pass@1
      verified: false
---

# AIronMind/stable-code-instruct-3b-Q4_K_M-GGUF
This model was converted to GGUF format from [`stabilityai/stable-code-instruct-3b`](https://huggingface.co/stabilityai/stable-code-instruct-3b) 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/stabilityai/stable-code-instruct-3b) for more details on the model.

## 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 AIronMind/stable-code-instruct-3b-Q4_K_M-GGUF --hf-file stable-code-instruct-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo AIronMind/stable-code-instruct-3b-Q4_K_M-GGUF --hf-file stable-code-instruct-3b-q4_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 AIronMind/stable-code-instruct-3b-Q4_K_M-GGUF --hf-file stable-code-instruct-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo AIronMind/stable-code-instruct-3b-Q4_K_M-GGUF --hf-file stable-code-instruct-3b-q4_k_m.gguf -c 2048
```