README.md

---
license: apache-2.0
inference: false
---

# MistralLite Model

MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capblities of processing long context (up to 36K 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)](https://github.com/huggingface/text-generation-inference) 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](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library.

MistralLight evolves from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Quantization | Max context length| RotaryEmbedding adaptation| Sliding Window Size|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|
| Mistral-7B-v0.1 | No | No | 36K | rope_theta = 10000 | 4096 |
| MistralLight | Yes | No | 36K | **rope_theta = 1000000** | **16384** |

## Motivation of Developing MistralLite

Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/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](https://lmsys.org/blog/2023-06-29-longchat/) ###
|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](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results) ###

|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](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101) ###

|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](https://nyu-mll.github.io/quality/) ###
|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](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
  - [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled)
  - [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions)
  - [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **Supported Serving Framework:**
  - [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0)
  - [vLLM](https://github.com/vllm-project/vllm)
  - [HuggingFace transformers](https://huggingface.co/docs/transformers/index)
  - [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container)
- **Model License:** Apache 2.0
- **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues)

## How to Use MistralFlite from Python Code ##
### Install the necessary packages

Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, and [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later.

```shell
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

```python
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](https://pypi.org/project/sagemaker/) 2.192.1 or later.

```shell
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.
```python
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:

```python
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](https://pypi.org/project/boto3/), and the example code is shown as below:

```python
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:

```shell
--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):

```shell
pip3 install huggingface-hub==0.17.0
```

```python
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](https://vllm.readthedocs.io/en/latest/).

### Using vLLM as a server ###
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
```shell
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:

```python
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.