Instructions to use ResplendentAI/Flora_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ResplendentAI/Flora_7B with Transformers:

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

pipe = pipeline("text-generation", model="ResplendentAI/Flora_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ResplendentAI/Flora_7B")
model = AutoModelForCausalLM.from_pretrained("ResplendentAI/Flora_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ResplendentAI/Flora_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ResplendentAI/Flora_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ResplendentAI/Flora_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ResplendentAI/Flora_7B

SGLang

How to use ResplendentAI/Flora_7B 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 "ResplendentAI/Flora_7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ResplendentAI/Flora_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ResplendentAI/Flora_7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ResplendentAI/Flora_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ResplendentAI/Flora_7B with Docker Model Runner:
```
docker model run hf.co/ResplendentAI/Flora_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Flora

Quants available here:

https://huggingface.co/bartowski/Flora_7B-GGUF

https://huggingface.co/bartowski/Flora_7B-exl2

https://huggingface.co/solidrust/Flora-7B-AWQ

Configuration

The following YAML configuration was used to produce this model:

merge_method: linear
models:
  - model: jeiku/FloraBase+jeiku/Synthetic_Soul_1k_Mistral_128
    parameters:
      weight: 1       
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.26
AI2 Reasoning Challenge (25-Shot)	72.10
HellaSwag (10-Shot)	88.31
MMLU (5-Shot)	64.16
TruthfulQA (0-shot)	71.19
Winogrande (5-shot)	84.45
GSM8k (5-shot)	65.35