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

Libraries

How to use wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0")
model = AutoModelForCausalLM.from_pretrained("wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0")
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]:]))

PEFT
How to use wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0 with PEFT:
```
Task type is invalid.
```
Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0

SGLang

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

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

Mistral-7B-Instruct-v0.3-flora-v0

This repository contains a full merged model produced by applying a PEFT LoRA adapter to its base model. It is intended for reproducible evaluation without requiring a separate adapter loading path.

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Run the bundled FSL evaluation wrapper:

bash fsl/src/evaluation/overall_eval.sh \
    --model_name wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0 \
    --model_type instruct \
    --max_samples 100 \
    --output_root fsl/results/evaluation/Mistral-7B-Instruct-v0.3-flora-v0

Training Procedure

Procedure: Federated LoRA fine-tuning followed by adapter aggregation. The resulting PEFT LoRA adapter was merged into the base model for reproducible evaluation.
Algorithm: flora
Training data: data_hetero_with_4_tasks
Number of clients: 8
Communication round/checkpoint: 3
Local epochs: 3
Local batch size: 256
Local micro batch size: 32
Local learning rate: 0.0003

Original training command:

Not specified

Merge Metadata

Base model: mistralai/Mistral-7B-Instruct-v0.3
Adapter source: /NHNHOME/0226010080_A/BASE/jongbokwon/FLS/fsl/outputs/flora/8/2
PEFT type: LORA
Task type: CAUSAL_LM
LoRA rank: 128
LoRA alpha: 128
Target modules: k_proj, v_proj, down_proj, up_proj, gate_proj, o_proj, q_proj
Merged at UTC: 2026-05-21T13:31:22.398478+00:00

Framework Versions

python: 3.10.20
platform: Linux-6.8.0-100-generic-x86_64-with-glibc2.39
torch: 2.11.0+cu130
cuda: 13.0
transformers: 4.57.1
peft: 0.19.1
accelerate: 1.13.0
huggingface_hub: 0.36.2
safetensors: 0.7.0
vllm: 0.20.2
wandb: 0.27.0

Notes

This is a merged full-weight model, not an adapter-only checkpoint.
Redistribution/access should follow the base model license and access policy.
merge_info.json in this repository records the adapter and merge configuration.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for wvnvwn/Mistral-7B-Instruct-v0.3-flora-v0

Base model

mistralai/Mistral-7B-v0.3

Finetuned

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

Adapter

(925)

this model