Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_M-GGUF

This model was converted to GGUF format from natong19/Mistral-Nemo-Instruct-2407-abliterated using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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))

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_m.gguf -c 2048