--- language: - en - fr - de - es - it - pt - ru - zh - ja license: apache-2.0 base_model: mistralai/Mistral-Nemo-Instruct-2407 extra_gated_description: If you want to learn more about how we process your personal data, please read our Privacy Policy. tags: - llama-cpp - gguf-my-repo --- # Triangle104/Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF This model was converted to GGUF format from [`mistralai/Mistral-Nemo-Instruct-2407`](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) 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/mistralai/Mistral-Nemo-Instruct-2407) for more details on the model. --- Model details: - The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size. For more details about this model please refer to our release blog post. Key features Released under the Apache 2 License Pre-trained and instructed versions Trained with a 128k context window Trained on a large proportion of multilingual and code data Drop-in replacement of Mistral 7B Model Architecture - Mistral Nemo is a transformer model, with the following architecture choices: Layers: 40 Dim: 5,120 Head dim: 128 Hidden dim: 14,336 Activation Function: SwiGLU Number of heads: 32 Number of kv-heads: 8 (GQA) Vocabulary size: 2**17 ~= 128k Rotary embeddings (theta = 1M) Metrics Main Benchmarks Benchmark Score HellaSwag (0-shot) 83.5% Winogrande (0-shot) 76.8% OpenBookQA (0-shot) 60.6% CommonSenseQA (0-shot) 70.4% TruthfulQA (0-shot) 50.3% MMLU (5-shot) 68.0% TriviaQA (5-shot) 73.8% NaturalQuestions (5-shot) 31.2% Multilingual Benchmarks (MMLU) Language Score French 62.3% German 62.7% Spanish 64.6% Italian 61.3% Portuguese 63.3% Russian 59.2% Chinese 59.0% Japanese 59.0% Usage The model can be used with three different frameworks mistral_inference: See here transformers: See here NeMo: See nvidia/Mistral-NeMo-12B-Instruct Mistral Inference - Install - It is recommended to use mistralai/Mistral-Nemo-Instruct-2407 with mistral-inference. For HF transformers code snippets, please keep scrolling. pip install mistral_inference Download - from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Nemo-Instruct') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) Chat - After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using mistral-chat $HOME/mistral_models/Nemo-Instruct --instruct --max_tokens 256 --temperature 0.35 E.g. Try out something like: How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar. Instruct following - from mistral_inference.transformer import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json") model = Transformer.from_folder(mistral_models_path) prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar." completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)]) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) print(result) Function calling - from mistral_common.protocol.instruct.tool_calls import Function, Tool from mistral_inference.transformer import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json") model = Transformer.from_folder(mistral_models_path) completion_request = ChatCompletionRequest( tools=[ Tool( function=Function( name="get_current_weather", description="Get the current weather", parameters={ "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location.", }, }, "required": ["location", "format"], }, ) ) ], messages=[ UserMessage(content="What's the weather like today in Paris?"), ], ) tokens = tokenizer.encode_chat_completion(completion_request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) print(result) Transformers - NOTE: Until a new release has been made, you need to install transformers from source: pip install git+https://github.com/huggingface/transformers.git 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-Nemo-Instruct-2407",max_new_tokens=128) chatbot(messages) Function calling with transformers - To use this example, you'll need transformers version 4.42.0 or higher. Please see the function calling guide in the transformers docs for more information. from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "mistralai/Mistral-Nemo-Instruct-2407" tokenizer = AutoTokenizer.from_pretrained(model_id) def get_current_weather(location: str, format: str): """ Get the current weather Args: location: The city and state, e.g. San Francisco, CA format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"]) """ pass conversation = [{"role": "user", "content": "What's the weather like in Paris?"}] tools = [get_current_weather] # format and tokenize the tool use prompt inputs = tokenizer.apply_chat_template( conversation, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt", ) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") inputs.to(model.device) outputs = model.generate(**inputs, max_new_tokens=1000) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool results to the chat history so that the model can use them in its next generation. For a full tool calling example, please see the function calling guide, and note that Mistral does use tool call IDs, so these must be included in your tool calls and tool results. They should be exactly 9 alphanumeric characters. Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3. Limitations - The Mistral Nemo Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall --- ## 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-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-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 Triangle104/Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-Q4_K_M-GGUF --hf-file mistral-nemo-instruct-2407-q4_k_m.gguf -c 2048 ```