---
base_model: GreenerPastures/Golden-Curry-12B
datasets:
- Mielikki/Erebus-87k
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- NewEden/Kalo-Opus-Instruct-22k-Refusal-Murdered
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- NewEden/Gryphe-Sonnet-3.5-35k-Subset
- Nitral-AI/GU_Instruct-ShareGPT
- Nitral-AI/Medical_Instruct-ShareGPT
- AquaV/Resistance-Sharegpt
- AquaV/US-Army-Survival-Sharegpt
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- ResplendentAI/bluemoon
- hardlyworking/openerotica-freedomrp-sharegpt-system
- MinervaAI/Aesir-Preview
- anthracite-core/c2_logs_32k_v1.1
- Nitral-AI/Creative_Writing-ShareGPT
- PJMixers/lodrick-the-lafted_OpusStories-Story2Prompt-ShareGPT
- NewEden/Opus-accepted-hermes-rejected-shuffled
language:
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---

# hardlyworking/Golden-Curry-12B-Q3_K_M-GGUF
This model was converted to GGUF format from [`GreenerPastures/Golden-Curry-12B`](https://huggingface.co/GreenerPastures/Golden-Curry-12B) 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/GreenerPastures/Golden-Curry-12B) 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 hardlyworking/Golden-Curry-12B-Q3_K_M-GGUF --hf-file golden-curry-12b-q3_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo hardlyworking/Golden-Curry-12B-Q3_K_M-GGUF --hf-file golden-curry-12b-q3_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 hardlyworking/Golden-Curry-12B-Q3_K_M-GGUF --hf-file golden-curry-12b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo hardlyworking/Golden-Curry-12B-Q3_K_M-GGUF --hf-file golden-curry-12b-q3_k_m.gguf -c 2048
```