Instructions to use Rijgersberg/Mistral-7B-v0.1-chat-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rijgersberg/Mistral-7B-v0.1-chat-nl with Transformers:

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

pipe = pipeline("text-generation", model="Rijgersberg/Mistral-7B-v0.1-chat-nl")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rijgersberg/Mistral-7B-v0.1-chat-nl")
model = AutoModelForCausalLM.from_pretrained("Rijgersberg/Mistral-7B-v0.1-chat-nl", device_map="auto")
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 Settings

vLLM

How to use Rijgersberg/Mistral-7B-v0.1-chat-nl with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rijgersberg/Mistral-7B-v0.1-chat-nl

SGLang

How to use Rijgersberg/Mistral-7B-v0.1-chat-nl 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 "Rijgersberg/Mistral-7B-v0.1-chat-nl" \
    --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": "Rijgersberg/Mistral-7B-v0.1-chat-nl",
		"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 "Rijgersberg/Mistral-7B-v0.1-chat-nl" \
        --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": "Rijgersberg/Mistral-7B-v0.1-chat-nl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rijgersberg/Mistral-7B-v0.1-chat-nl with Docker Model Runner:
```
docker model run hf.co/Rijgersberg/Mistral-7B-v0.1-chat-nl
```

Mistral-7B-v0.1-chat-nl

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the Rijgersberg/no_robots_nl and Rijgersberg/ultrachat_10k_nl datasets. It achieves the following results on the evaluation set:

Loss: 1.0263

Model description

In order to investigate the effect of pretraining Rijgersberg/GEITje-7B on the finetuning of Rijgersberg/GEITje-7B-chat, I also subjected the base model Mistral 7B v0.1 to the exact same training. This model is called Mistral-7B-v0.1-chat-nl.

More info

Read more about GEITje and GEITje-chat in the 📄 README on GitHub.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.2404	0.2	236	1.1166
1.2103	0.4	472	1.1101
1.0357	0.6	708	1.0739
1.27	0.8	944	1.0540
1.3557	1.0	1180	1.0330
0.7919	1.2	1416	1.0368
0.8701	1.4	1652	1.0193
0.8851	1.6	1888	1.0009
0.7562	1.8	2124	0.9791
0.6838	2.0	2360	0.9823
0.5011	2.2	2596	1.0271
0.4495	2.39	2832	1.0267
0.5625	2.59	3068	1.0250
0.4486	2.79	3304	1.0262
0.5706	2.99	3540	1.0263

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Rijgersberg/Mistral-7B-v0.1-chat-nl

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(946)

this model

Datasets used to train Rijgersberg/Mistral-7B-v0.1-chat-nl

Collection including Rijgersberg/Mistral-7B-v0.1-chat-nl

GEITje 7B: A Large Open Dutch Language Model

Collection

All models and datasets relating to GEITje • 7 items • Updated May 28 • 5