--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - ExLlamaV2 - 8bit - Mistral - Mistral-7B - quantized - exl2 - 8.0-bpw --- # Model Card for alokabhishek/Mistral-7B-Instruct-v0.2-8.0-bpw-exl2 This repo contains 8-bit quantized (using ExLlamaV2) model Mistral AI_'s Mistral-7B-Instruct-v0.2 ## Model Details - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ### About quantization using ExLlamaV2 - ExLlamaV2 github repo: [ExLlamaV2 github repo](https://github.com/turboderp/exllamav2) # How to Get Started with the Model Use the code below to get started with the model. ## How to run from Python code #### First install the package ```shell # Install ExLLamaV2 !git clone https://github.com/turboderp/exllamav2 !pip install -e exllamav2 ``` #### Import ```python from huggingface_hub import login, HfApi, create_repo from torch import bfloat16 import locale import torch import os ``` #### set up variables ```python # Define the model ID for the desired model model_id = "alokabhishek/Mistral-7B-Instruct-v0.2-8.0-bpw-exl2" BPW = 8.0 # define variables model_name = model_id.split("/")[-1] ``` #### Download the quantized model ```shell !git-lfs install # download the model to loacl directory !git clone https://{username}:{HF_TOKEN}@huggingface.co/{model_id} {model_name} ``` #### Run Inference on quantized model using ```shell # Run model !python exllamav2/test_inference.py -m {model_name}/ -p "Tell me a funny joke about Large Language Models meeting a Blackhole in an intergalactic Bar." ``` ```python import sys, os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from exllamav2 import ( ExLlamaV2, ExLlamaV2Config, ExLlamaV2Cache, ExLlamaV2Tokenizer, ) from exllamav2.generator import ExLlamaV2BaseGenerator, ExLlamaV2Sampler import time # Initialize model and cache model_directory = "/model_path/Mistral-7B-Instruct-v0.2-8.0-bpw-exl2/" print("Loading model: " + model_directory) config = ExLlamaV2Config(model_directory) model = ExLlamaV2(config) cache = ExLlamaV2Cache(model, lazy=True) model.load_autosplit(cache) tokenizer = ExLlamaV2Tokenizer(config) # Initialize generator generator = ExLlamaV2BaseGenerator(model, cache, tokenizer) # Generate some text settings = ExLlamaV2Sampler.Settings() settings.temperature = 0.85 settings.top_k = 50 settings.top_p = 0.8 settings.token_repetition_penalty = 1.01 settings.disallow_tokens(tokenizer, [tokenizer.eos_token_id]) prompt = "Tell me a funny joke about Large Language Models meeting a Blackhole in an intergalactic Bar." max_new_tokens = 512 generator.warmup() time_begin = time.time() output = generator.generate_simple(prompt, settings, max_new_tokens, seed=1234) time_end = time.time() time_total = time_end - time_begin print(output) print() print(f"Response generated in {time_total:.2f} seconds") ``` # Model Card for Source Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2. Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1 - 32k context window (vs 8k context in v0.1) - Rope-theta = 1e6 - No Sliding-Window Attention For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B 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, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.