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---
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
base_model: natong19/Mistral-Nemo-Instruct-2407-abliterated
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`natong19/Mistral-Nemo-Instruct-2407-abliterated`](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-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/natong19/Mistral-Nemo-Instruct-2407-abliterated) for more details on the model.
---
Model details:
-
Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.
Key features
Trained with a 128k context window
Trained on a large proportion of multilingual and code data
Drop-in replacement of Mistral 7B
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "natong19/Mistral-Nemo-Instruct-2407-abliterated"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(model_id)
conversation = [{"role": "user", "content": "Where's the capital of France?"}]
tool_use_prompt = tokenizer.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(tool_use_prompt, return_tensors="pt", return_token_type_ids=False).to(device)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True))
---
## 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-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-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/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -c 2048
```
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