Instructions to use seyabde/mistral_7b_yo_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seyabde/mistral_7b_yo_instruct with Transformers:

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

pipe = pipeline("text-generation", model="seyabde/mistral_7b_yo_instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("seyabde/mistral_7b_yo_instruct")
model = AutoModelForCausalLM.from_pretrained("seyabde/mistral_7b_yo_instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use seyabde/mistral_7b_yo_instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seyabde/mistral_7b_yo_instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seyabde/mistral_7b_yo_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/seyabde/mistral_7b_yo_instruct

SGLang

How to use seyabde/mistral_7b_yo_instruct 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 "seyabde/mistral_7b_yo_instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seyabde/mistral_7b_yo_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "seyabde/mistral_7b_yo_instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seyabde/mistral_7b_yo_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use seyabde/mistral_7b_yo_instruct with Docker Model Runner:
```
docker model run hf.co/seyabde/mistral_7b_yo_instruct
```

Model Description

mistral_7b_yo_instruct is a text generation model in Yorùbá.

Intended uses & limitations

How to use


import requests

API_URL = "https://i8nykns7vw253vx3.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
    "Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    "Content-Type": "application/json"
}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Prompt content: "Pẹlẹ o. Bawo ni o se wa?" ("Hello. How are you?")	
output = query({
    "inputs": "Pẹlẹ o. Bawo ni o se wa?",
})

# Model response: "O dabo. O jẹ ọjọ ti o dara." ("I am safe. It was a good day.")
print(output)

Eval results

Coming soon

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.

Training data

This model is fine-tuned on 60k+ instruction-following demonstrations built from an aggregation of datasets (AfriQA, XLSum, MENYO-20k), and translations of Alpaca-gpt4).

Use and safety

We emphasize that mistral_7b_yo_instruct is intended only for research purposes and is not ready to be deployed for general use, namely because we have not designed adequate safety measures.

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

F16

seyabde
/

mistral_7b_yo_instruct