Instructions to use rootsec1/mistral-7B-it-aipi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootsec1/mistral-7B-it-aipi with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rootsec1/mistral-7B-it-aipi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rootsec1/mistral-7B-it-aipi")
model = AutoModelForCausalLM.from_pretrained("rootsec1/mistral-7B-it-aipi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rootsec1/mistral-7B-it-aipi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rootsec1/mistral-7B-it-aipi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/mistral-7B-it-aipi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rootsec1/mistral-7B-it-aipi

SGLang

How to use rootsec1/mistral-7B-it-aipi with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rootsec1/mistral-7B-it-aipi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/mistral-7B-it-aipi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rootsec1/mistral-7B-it-aipi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/mistral-7B-it-aipi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rootsec1/mistral-7B-it-aipi with Docker Model Runner:
```
docker model run hf.co/rootsec1/mistral-7B-it-aipi
```

Fine-tuning Mistral-7B-v0.1 on Symbolic Instruction Tuning Dataset

This repository contains the fine-tuned version of the mistralai/Mistral-7B-v0.1 model on the sail/symbolic-instruction-tuning dataset. The objective of this fine-tuning process is to specialize the pre-trained model for improved performance on tasks that require understanding and processing symbolic instructions.

Model Description

Mistral-7B-v0.1 is a transformer-based language model pre-trained on a diverse corpus of text. Our fine-tuning process aims to leverage this pre-trained model and further optimize it for the symbolic instruction tuning task provided by the sail/symbolic-instruction-tuning dataset.

Dataset

The sail/symbolic-instruction-tuning dataset is designed to test a model's ability to comprehend and execute symbolic instructions. It consists of a series of tasks that require the model to manipulate symbolic inputs according to specific instructions.

Fine-tuning Process

The fine-tuning process involves the following steps:

Environment Setup: Ensure that your environment has all the necessary dependencies installed, including transformers and datasets from Hugging Face.
Data Preparation: Load the sail/symbolic-instruction-tuning dataset using the datasets library and prepare it for the training process, including any necessary preprocessing steps.
Model Initialization: Load the pre-trained mistralai/Mistral-7B-v0.1 model and prepare it for fine-tuning.
Training: Fine-tune the model on the prepared dataset using an appropriate training script. This involves setting hyperparameters, training loops, and logging.
Evaluation: Evaluate the fine-tuned model's performance on a validation set to ensure that it has learned the task effectively.
Saving and Sharing: Save the fine-tuned model and upload it to the Hugging Face model hub for easy sharing and reuse.

Usage

The fine-tuned model can be loaded from the Hugging Face model hub using the transformers library as follows:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "rootsec1/mistal-7B-it-aipi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
inputs = tokenizer("Example input", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

7B params

Tensor type

BF16