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---
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
base_model: mistralai/Mistral-Large-Instruct-2407
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
pipeline_tag: text-generation
tags:
- chat
---
# Mistral-Large-Instruct-2407 FP8
This repository contains the quantized weights for [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407).
The weights have been converted to FP8 format, with FP8 weights, FP8 activations, and FP8 KV cache. You can use either [vLLM](https://github.com/vllm-project/vllm) or [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine) to load this model.
## Quantization Method
The library used is [llm-compressor](https://github.com/vllm-project/llm-compressor).
```console
pip install llmcompressor
```
Then run this script:
```py
from datasets import load_dataset
from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
MODEL_ID = "mistralai/Mistral-Large-Instruct-2407"
model = SparseAutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# Select calibration dataset.
DATASET_ID = "HuggingFaceH4/ultrachat_200k" # Or use your own dataset
DATASET_SPLIT = "train_sft"
# You can increase the the number of samples to increase accuracy
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def process_and_tokenize(example):
text = tokenizer.apply_chat_template(example["messages"], tokenize=False)
return tokenizer(
text,
padding=False,
max_length=MAX_SEQUENCE_LENGTH,
truncation=True,
add_special_tokens=False,
)
ds = ds.map(process_and_tokenize, remove_columns=ds.column_names)
# Configure the quantization algorithm and scheme.
# In this case, we:
# * quantize the weights to fp8 with per-tensor scales
# * quantize the activations to fp8 with per-tensor scales
# * quantize the kv cache to fp8 with per-tensor scales
recipe = """
quant_stage:
quant_modifiers:
QuantizationModifier:
ignore: ["lm_head"]
config_groups:
group_0:
weights:
num_bits: 8
type: float
strategy: tensor
dynamic: false
symmetric: true
input_activations:
num_bits: 8
type: float
strategy: tensor
dynamic: false
symmetric: true
targets: ["Linear"]
kv_cache_scheme:
num_bits: 8
type: float
strategy: tensor
dynamic: false
symmetric: true
"""
# Apply algorithms.
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)
# Save to disk compressed.
SAVE_DIR = "./Mistral-Large-Instruct-2407-FP8"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
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