license: apache-2.0
inference: false
MistralLite Model
MistralLite is a fine-tuned Mistral-7B-v0.1 language model, with enhanced capblities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLight is able to perform signficantly better on several long context retrieve and answering tasks, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS g5.2x instance with Sagemaker Huggingface Text Generation Inference (TGI) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like vLLM, and you can use MistralLite in Python by using the HuggingFace transformers and FlashAttention-2 library.
MistralLight evolves from Mistral-7B-v0.1, and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size| |----------|-------------:|-------------:|------------:|-----------:|-----------:| | Mistral-7B-v0.1 | No | 32K | rope_theta = 10000 | 4096 | | MistralLite | Yes | 32K | rope_theta = 1000000 | 16384 |
Motivation of Developing MistralLite
Since the release of Mistral-7B-Instruct-v0.1, the model became increasingly popular because its strong performance on a wide range of benchmarks. But most of the benchmarks are evaluated on short context, and not much has been investigated on its performance on long context tasks. Then We evaluated Mistral-7B-Instruct-v0.1 against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context. Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens, there were some discrepencies on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and got Mistrallite. The model managed to signifantly boost the performance of long context handling over Mistral-7B-Instruct-v0.1. The detailed long context evalutaion results are as below:
Topic Retrieval
| Model Name | Input length | Input length | Input length | Input length | Input length |
|---|---|---|---|---|---|
| 2851 | 5568 | 8313 | 11044 | 13780 | |
| Mistral-7B-Instruct-v0.1 | 90% | 0% | 0% | 0% | 0% |
| MistralLite | 100% | 100% | 100% | 100% | 98% |
Line Retrieval
| Model Name | Input length | Input length | Input length | Input length | Input length | Input length |
|---|---|---|---|---|---|---|
| 3818 | 5661 | 7505 | 9354 | 11188 | 12657 | |
| Mistral-7B-Instruct-v0.1 | 98% | 62% | 42% | 42% | 32% | 30% |
| MistralLite | 98% | 92% | 88% | 76% | 70% | 60% |
Pass key Retrieval
| Model Name | Input length | Input length | Input length | Input length |
|---|---|---|---|---|
| 3264 | 5396 | 8329 | 10197 | |
| Mistral-7B-Instruct-v0.1 | 100% | 50% | 20% | 30% |
| MistralLite | 100% | 100% | 100% | 100% |
Question Answering with Long Input Texts
| Model Name | Test set Accuracy | Hard subset Accuracy |
|---|---|---|
| Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% |
| MistralLite | 64.4% | 56.2% |
Model Details
- Developed by: AWS Contributors
- Model type: Mistral-7B-v0.1
- Language: English
- Finetuned from weights: Mistral-7B-v0.1
- Finetuned on data:
- Supported Serving Framework:
- Model License: Apache 2.0
- Contact: GitHub issues
How to Use MistralFlite from Python Code
Install the necessary packages
Requires: transformers 4.34.0 or later, and flash-attn 2.3.1.post1 or later.
pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
You can then try the following example code
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "amazon/MistralLite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True,
device_map="auto",)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
sequences = pipeline(
prompt,
max_new_tokens=200,
do_sample=False,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"{seq['generated_text']}")
Important - Use the prompt template below for MistralLite:
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
How to Deploy MistralFlite on Amazon SageMaker
Install the necessary packages
Requires: sagemaker 2.192.1 or later.
pip install sagemaker==2.192.1
Deploy the Model as A SageMaker Endpoint
To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
image_uri = get_huggingface_llm_image_uri(
backend="huggingface", # or lmi
region=region,
version="1.1.0"
)
model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
hub = {
'HF_MODEL_ID':'amazon/MistralLite',
'HF_TASK':'text-generation',
'SM_NUM_GPUS':'1',
'HF_MODEL_QUANTIZE':'true'
}
model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name=model_name
)
Perform Inference
To call the endpoint, please follow the example code as below:
input_data = {
"inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
"parameters": {
"do_sample": False,
"max_new_tokens": 100,
}
}
predictor.predict(input_data)
or via boto3, and the example code is shown as below:
import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
client = boto3.client("sagemaker-runtime")
payload = {"inputs": prompt,
"parameters": parameters}
response = client.invoke_endpoint(EndpointName=endpoint_name,
Body=json.dumps(payload),
ContentType="application/json")
output = json.loads(response["Body"].read().decode())
result = output[0]["generated_text"]
return result
client = boto3.client("sagemaker-runtime")
parameters = {
"max_new_tokens": 250,
"do_sample": True,
"temperature": None,
"use_cache": True,
"seed": 1,
}
endpoint_name = "your-endpoint-name-here""
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, paramters)
print(result)
How to Serve MistralFlite on TGI
Start TGI server
Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0
Example Docker parameters:
--model-id amazon/MistralLite --port 3000 --max-input-length 8192 --max-total-tokens 16384 --max-batch-prefill-tokens 16384
Perform Inference
Example Python code for inference with TGI (requires huggingface-hub 0.17.0 or later):
pip3 install huggingface-hub==0.17.0
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=100,
do_sample=False,
temperature=None,
)
print(f"Model output: {response}")
Important - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
How to Serve MistralFlite on vLLM
Documentation on installing and using vLLM can be found here.
Using vLLM as a server
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
python3 python -m vllm.entrypoints.api_server --model amazon/MistralLite
Using vLLM in Python Code
When using vLLM from Python code, Please see the example code as below:
from vllm import LLM, SamplingParams
prompts = [
"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)
llm = LLM(model="amazon/MistralLite",)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
Limitations
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.