Instructions to use KnutJaegersberg/Mistral-7B-EssayWriter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KnutJaegersberg/Mistral-7B-EssayWriter with Transformers:

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

pipe = pipeline("text-generation", model="KnutJaegersberg/Mistral-7B-EssayWriter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/Mistral-7B-EssayWriter")
model = AutoModelForCausalLM.from_pretrained("KnutJaegersberg/Mistral-7B-EssayWriter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KnutJaegersberg/Mistral-7B-EssayWriter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KnutJaegersberg/Mistral-7B-EssayWriter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KnutJaegersberg/Mistral-7B-EssayWriter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KnutJaegersberg/Mistral-7B-EssayWriter

SGLang

How to use KnutJaegersberg/Mistral-7B-EssayWriter 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 "KnutJaegersberg/Mistral-7B-EssayWriter" \
    --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": "KnutJaegersberg/Mistral-7B-EssayWriter",
		"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 "KnutJaegersberg/Mistral-7B-EssayWriter" \
        --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": "KnutJaegersberg/Mistral-7B-EssayWriter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KnutJaegersberg/Mistral-7B-EssayWriter with Docker Model Runner:
```
docker model run hf.co/KnutJaegersberg/Mistral-7B-EssayWriter
```

Prompt Example:

### Instruction: 
Provide the inputted essay that when summarized resulted in the following summary : The organization's learning and development practices are poor as they involve manual orientation from experienced workers. New employees are not advised of their roles in the organization before assignment of tasks. The orientation process takes long due to lack of a well-coordinated orientation programs.
### Response:

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

F16

KnutJaegersberg
/

Mistral-7B-EssayWriter

Dataset used to train KnutJaegersberg/Mistral-7B-EssayWriter