Instructions to use ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="ignos/LeoScorpius-GreenNode-Alpaca-7B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ignos/LeoScorpius-GreenNode-Alpaca-7B-v1")
model = AutoModelForCausalLM.from_pretrained("ignos/LeoScorpius-GreenNode-Alpaca-7B-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1

SGLang

How to use ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 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 "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1" \
    --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": "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
		"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 "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1" \
        --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": "ignos/LeoScorpius-GreenNode-Alpaca-7B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ignos/LeoScorpius-GreenNode-Alpaca-7B-v1 with Docker Model Runner:
```
docker model run hf.co/ignos/LeoScorpius-GreenNode-Alpaca-7B-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

This model is a finetuning of other models based on mistralai/Mistral-7B-v0.1.

Model Details

Model Description

The model has been generated from the merging of the models viethq188/LeoScorpius-7B-Chat-DPO and GreenNode/GreenNodeLM-7B-v1olet and a later finetuning with an Alpaca dataset tatsu-lab/alpaca.

Developed by: Ignos
Model type: Mistral
License: Apache-2.0

Uses

Model created for the comparison of behaviors and metrics with respect to the base model, as well as the comparison with other models that using the same base have been finetuning on other different datasets.

Bias, Risks, and Limitations

The same bias, risks and limitations from base models.

Training Details

Training Data

tatsu-lab/alpaca

Training Procedure

Training with QLoRA approach and merging with base model.

Results

Huggingface evaluation pending

Summary

Technical Specifications

Model Architecture and Objective

Models based on Mistral Architecture

Compute Infrastructure

Training on RunPod

Hardware

4 x Nvidia RTX 4090
64 vCPU 503 GB RAM

Software

Mergekit (main)
Axolotl 0.3.0

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: bitsandbytes
load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: bfloat16

Framework versions

PEFT 0.6.0

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Quantizations

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