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Mistral-7B-Instruct-v0.2-4.0-bpw-exl2

Text Generation
Transformers
PyTorch
Safetensors
mistral
ExLlamaV2
4bit
Mistral
Mistral-7B
quantized
exl2
4.0-bpw
conversational
text-generation-inference
Inference Endpoints
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Mistral-7B-Instruct-v0.2-4.0-bpw-exl2 / README.md
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metadata
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - ExLlamaV2
  - 4bit
  - Mistral
  - Mistral-7B
  - quantized
  - exl2
  - 4.0-bpw

Model Card for alokabhishek/Mistral-7B-Instruct-v0.2-4.0-bpw-exl2

This repo contains 4-bit quantized (using ExLlamaV2) model Mistral AI_'s Mistral-7B-Instruct-v0.2

Model Details

About 4 bit quantization using 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 

# Install ExLLamaV2
!git clone https://github.com/turboderp/exllamav2
!pip install -e exllamav2

Import 

from huggingface_hub import login, HfApi, create_repo
from torch import bfloat16
import locale
import torch
import os

set up variables 

# Define the model ID for the desired model
model_id = "alokabhishek/Mistral-7B-Instruct-v0.2-4.0-bpw-exl2"
BPW = 4.0

# define variables
model_name =  model_id.split("/")[-1]

Download the quantized model 

!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 

# 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."


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-v0.2-4.0-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)

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Evaluation

Metrics

[More Information Needed]

Results

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Original Model Card for 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 and release blog post.

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 = "<s>[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!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

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.