---
license: apache-2.0
base_model: huihui-ai/Mistral-7B-Instruct-v0.3-abliterated
extra_gated_description: If you want to learn more about how we process your personal
  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- Text Generation
- Transformers
- Safetensors
- conversational
- text-generation-inference
- abliterated
- uncensored
- Inference Endpoints
- llama-cpp
- gguf-my-repo
---

# Triangle104/Mistral-7B-Instruct-v0.3-abliterated-Q4_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/Mistral-7B-Instruct-v0.3-abliterated`](https://huggingface.co/huihui-ai/Mistral-7B-Instruct-v0.3-abliterated) 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/huihui-ai/Mistral-7B-Instruct-v0.3-abliterated) for more details on the model.

---
Model details:
-
This is an uncensored version of mistralai/Mistral-7B-Instruct-v0.3 created with abliteration (see remove-refusals-with-transformers to know more about it).

If the desired result is not achieved, you can clear the conversation and try again.
Generate with transformers

If you want to use Hugging Face transformers to generate text, you can do something like this.

from transformers import pipeline

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3-abliterated")
chatbot(messages)

---
## 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/Mistral-7B-Instruct-v0.3-abliterated-Q4_K_S-GGUF --hf-file mistral-7b-instruct-v0.3-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-7B-Instruct-v0.3-abliterated-Q4_K_S-GGUF --hf-file mistral-7b-instruct-v0.3-abliterated-q4_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/Mistral-7B-Instruct-v0.3-abliterated-Q4_K_S-GGUF --hf-file mistral-7b-instruct-v0.3-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Mistral-7B-Instruct-v0.3-abliterated-Q4_K_S-GGUF --hf-file mistral-7b-instruct-v0.3-abliterated-q4_k_s.gguf -c 2048
```