Instructions to use Guibibo/Mistral-7B-v0.3-FlashCards with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Guibibo/Mistral-7B-v0.3-FlashCards with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base_model, "Guibibo/Mistral-7B-v0.3-FlashCards")

Transformers

How to use Guibibo/Mistral-7B-v0.3-FlashCards with Transformers:

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

pipe = pipeline("text-generation", model="Guibibo/Mistral-7B-v0.3-FlashCards")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Guibibo/Mistral-7B-v0.3-FlashCards", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Guibibo/Mistral-7B-v0.3-FlashCards with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Guibibo/Mistral-7B-v0.3-FlashCards"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Guibibo/Mistral-7B-v0.3-FlashCards",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Guibibo/Mistral-7B-v0.3-FlashCards

SGLang

How to use Guibibo/Mistral-7B-v0.3-FlashCards 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 "Guibibo/Mistral-7B-v0.3-FlashCards" \
    --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": "Guibibo/Mistral-7B-v0.3-FlashCards",
		"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 "Guibibo/Mistral-7B-v0.3-FlashCards" \
        --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": "Guibibo/Mistral-7B-v0.3-FlashCards",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Guibibo/Mistral-7B-v0.3-FlashCards with Docker Model Runner:
```
docker model run hf.co/Guibibo/Mistral-7B-v0.3-FlashCards
```

Model Card for Model ID

This model creates Anki Flashcards from french texts.

Model Details

Model Description

This model takes as input chunks of texts and outputs flashcards in the format'Q: A:'. It was trained on a personal dataset of french cards.

Developed by: Guillaume Bertho
Language(s) (NLP): French
Finetuned from model Mistral-7B-Instruct-v0.3

Model Sources [optional]

Repository: https://github.com/GUIBOoO/ankify-local

Uses

Give the model chunks of french text (from 100 to 2000 tokens). This model doesn't perform well on mathematical expressions/

Bias, Risks, and Limitations

This model can sometimes generate non-self-contained cards.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MODEL_NAME = "Guibibo/Mistral-7B-v0.3-FlashCards"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype=torch.float16,
    device_map="auto"  # automatically uses GPU if available
)

prompt = """Victor Hugo, né le 7 ventôse an X (26 février 1802) à Besançon ..."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,   # adjust length
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("=== Generated Text ===")
print(generated_text)

Training Details

Training Data

Personal data. I will maybe upload the dataset later.

Training Hyperparameters

Training settings : Warmup ratio: 0.03 Gradient accumulation steps: 4 Learning rate: 1e-4 Number of epochs: 1 Learning rate scheduler: cosine Weight decay: 0.01 Mixed precision training enabled (fp16)

LoRA configuration: Rank (r): 64 LoRA alpha: 64 LoRA dropout: 0.05 Target modules: ["q_proj", "v_proj"] Task type: CAUSAL_LM

Tokenizer settings: Pad sequences to end-of-sequence (pad_to_eos: true)

PEFT 0.18.0

Downloads last month: 4

Model tree for Guibibo/Mistral-7B-v0.3-FlashCards

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(839)

this model