Instructions to use pbevan11/Mistral-Nemo-Baseline-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pbevan11/Mistral-Nemo-Baseline-SFT with Transformers:

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

pipe = pipeline("text-generation", model="pbevan11/Mistral-Nemo-Baseline-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pbevan11/Mistral-Nemo-Baseline-SFT")
model = AutoModelForCausalLM.from_pretrained("pbevan11/Mistral-Nemo-Baseline-SFT")
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 pbevan11/Mistral-Nemo-Baseline-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pbevan11/Mistral-Nemo-Baseline-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pbevan11/Mistral-Nemo-Baseline-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pbevan11/Mistral-Nemo-Baseline-SFT

SGLang

How to use pbevan11/Mistral-Nemo-Baseline-SFT 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 "pbevan11/Mistral-Nemo-Baseline-SFT" \
    --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": "pbevan11/Mistral-Nemo-Baseline-SFT",
		"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 "pbevan11/Mistral-Nemo-Baseline-SFT" \
        --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": "pbevan11/Mistral-Nemo-Baseline-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pbevan11/Mistral-Nemo-Baseline-SFT with Docker Model Runner:
```
docker model run hf.co/pbevan11/Mistral-Nemo-Baseline-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-Nemo-Instruct-Baseline-SFT

This model is a fine-tuned version of mistralai/Mistral-Nemo-Base-2407 on the pbevan11/ultrafeedback_binarized_multilingual dataset. It achieves the following results on the evaluation set:

Loss: 1.1750

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
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
1.6158	0.9455	13	1.3800
1.1061	1.9636	27	1.1854
0.9071	2.8364	39	1.1750

Framework versions

Transformers 4.45.1
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0

Downloads last month: 2

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for pbevan11/Mistral-Nemo-Baseline-SFT

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

(84)

this model

Quantizations

2 models

Dataset used to train pbevan11/Mistral-Nemo-Baseline-SFT

Collection including pbevan11/Mistral-Nemo-Baseline-SFT

Multilingual Constitutional AI

Collection

Blog: https://sites.google.com/view/multilingual-constitutional-ai • 11 items • Updated Mar 2 • 1