Instructions to use maxidl/Mistral-7B-v0.1-Instruct-sft-en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxidl/Mistral-7B-v0.1-Instruct-sft-en-de with Transformers:

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

pipe = pipeline("text-generation", model="maxidl/Mistral-7B-v0.1-Instruct-sft-en-de")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxidl/Mistral-7B-v0.1-Instruct-sft-en-de")
model = AutoModelForCausalLM.from_pretrained("maxidl/Mistral-7B-v0.1-Instruct-sft-en-de")
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 maxidl/Mistral-7B-v0.1-Instruct-sft-en-de with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maxidl/Mistral-7B-v0.1-Instruct-sft-en-de

SGLang

How to use maxidl/Mistral-7B-v0.1-Instruct-sft-en-de 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 "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de" \
    --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": "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de",
		"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 "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de" \
        --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": "maxidl/Mistral-7B-v0.1-Instruct-sft-en-de",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maxidl/Mistral-7B-v0.1-Instruct-sft-en-de with Docker Model Runner:
```
docker model run hf.co/maxidl/Mistral-7B-v0.1-Instruct-sft-en-de
```

Mistral-7B-v0.1-Instruct-sft-en-de

A full finetune of mistralai/Mistral-7B-v0.1 using a mix of English and German instruction data.

Dataset

source	#examples
teknium/OpenHermes-2.5	1001551
maxidl/OpenOrca-gpt4-de	119559
maxidl/MathInstruct-de	56793
maxidl/Capybara-de	15991
maxidl/math-prm-800k-de	12298
maxidl/wikihow-de	10103
maxidl/no_robots-de	9500
maxidl/lima-de	1030

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 32
total_train_batch_size: 128
total_eval_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 3

Training results

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for maxidl/Mistral-7B-v0.1-Instruct-sft-en-de

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1032)

this model