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Mistral-7B-Instruct-v0.2-neuron-4x2048-24-cores-2.18

Text Generation
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mistral
inferentia2
neuron
neuronx
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Mistral-7B-Instruct-v0.2-neuron-4x2048-24-cores-2.18
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---
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- mistral
- inferentia2
- neuron
- neuronx
license: apache-2.0
---
# Neuronx for [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - Updated Mistral 7B Model on [AWS Inferentia2](https://aws.amazon.com/ec2/instance-types/inf2/) Using AWS Neuron SDK version 2.18~

This model has been exported to the `neuron` format using specific `input_shapes` and `compiler` parameters detailed in the paragraphs below.

Please refer to the 🤗 `optimum-neuron` [documentation](https://huggingface.co/docs/optimum-neuron/main/en/guides/models#configuring-the-export-of-a-generative-model) for an explanation of these parameters.

Note: To compile the mistralai/Mistral-7B-Instruct-v0.2 on Inf2, you need to update the model config sliding_window (either file or model variable) from null to default 4096.

## Usage with 🤗 `TGI`
Refer to container image on [neuronx-tgi](https://gallery.ecr.aws/shtian/neuronx-tgi) Amazon ECR Public Gallery.
```shell
export HF_TOKEN="hf_xxx"

docker run -d -p 8080:80 \
       --name mistral-7b-neuronx-tgi \
       -v $(pwd)/data:/data \
       --device=/dev/neuron0 \
       --device=/dev/neuron1 \
       --device=/dev/neuron2 \
       --device=/dev/neuron3 \
       --device=/dev/neuron4 \
       --device=/dev/neuron5 \
       --device=/dev/neuron6 \
       --device=/dev/neuron7 \
       --device=/dev/neuron8 \
       --device=/dev/neuron9 \
       --device=/dev/neuron10 \
       --device=/dev/neuron11 \
       -e HF_TOKEN=${HF_TOKEN} \
       public.ecr.aws/shtian/neuronx-tgi:latest \
       --model-id davidshtian/Mistral-7B-Instruct-v0.2-neuron-1x2048-24-cores-2.18 \
       --max-batch-size 4 \
       --max-input-length 16 \
       --max-total-tokens 32

curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"Who are you?","parameters":{"max_new_tokens":16}}' \
    -H 'Content-Type: application/json'
```

## Usage with 🤗 `optimum-neuron pipeline`

```python
from optimum.neuron import pipeline

p = pipeline('text-generation', 'davidshtian/Mistral-7B-Instruct-v0.2-neuron-4x2048-24-cores-2.18')
p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50)

[{'generated_text': "My favorite place on earth is probably Paris, France, and if I were to go there
now I would take my partner on a romantic getaway where we could lay on the grass in the park,
eat delicious French cheeses and wine, and watch the sunset on the Seine river.'"}]
```

## Usage with 🤗 `optimum-neuron NeuronModelForCausalLM`

```python
import torch
from transformers import AutoTokenizer
from optimum.neuron import NeuronModelForCausalLM

model = NeuronModelForCausalLM.from_pretrained("davidshtian/Mistral-7B-Instruct-v0.2-neuron-4x2048-24-cores-2.18")

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer.pad_token_id = tokenizer.eos_token_id

def model_sample(input_prompt):
    input_prompt = "[INST] " + input_prompt + " [/INST]"

    tokens = tokenizer(input_prompt, return_tensors="pt")

    with torch.inference_mode():
        sample_output = model.generate(
            **tokens,
            do_sample=True,
            min_length=16,
            max_length=32,
            temperature=0.5,
            pad_token_id=tokenizer.eos_token_id
        )
        outputs = [tokenizer.decode(tok, skip_special_tokens=True) for tok in sample_output]

    res = outputs[0].split('[/INST]')[1].strip("</s>").strip()
    return(res + "\n")

print(model_sample("how are you today?"))
```

This repository contains tags specific to versions of `neuronx`. When using with 🤗 `optimum-neuron`, use the repo revision specific to the version of `neuronx` you are using, to load the right serialized checkpoints.

## Arguments passed during export

**input_shapes**

```json
{
  "batch_size": 4,
  "sequence_length": 2048,
}
```

**compiler_args**

```json
{
  "auto_cast_type": "bf16",
  "num_cores": 24,
}
```