Instructions to use hgloow/Merged-AGI-7B-EXL2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hgloow/Merged-AGI-7B-EXL2 with Transformers:

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

pipe = pipeline("text-generation", model="hgloow/Merged-AGI-7B-EXL2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hgloow/Merged-AGI-7B-EXL2")
model = AutoModelForCausalLM.from_pretrained("hgloow/Merged-AGI-7B-EXL2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hgloow/Merged-AGI-7B-EXL2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hgloow/Merged-AGI-7B-EXL2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hgloow/Merged-AGI-7B-EXL2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hgloow/Merged-AGI-7B-EXL2

SGLang

How to use hgloow/Merged-AGI-7B-EXL2 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 "hgloow/Merged-AGI-7B-EXL2" \
    --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": "hgloow/Merged-AGI-7B-EXL2",
		"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 "hgloow/Merged-AGI-7B-EXL2" \
        --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": "hgloow/Merged-AGI-7B-EXL2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hgloow/Merged-AGI-7B-EXL2 with Docker Model Runner:
```
docker model run hf.co/hgloow/Merged-AGI-7B-EXL2
```

Merged-AGI-7B

Model creator: Talha Rüzgar Akkuş
Original model: Merged AGI 7B

EXL2 Quants

Zipped Quantization (if you want to download a single file)

Calibration Dataset

wikitext-103-v1

Memory Usage

Measured using ExLlamav2_HF and 4096 max_seq_len with Oobabooga's Text Generation WebUI.

Branch	BPW	VRAM Usage	Description
3.0bpw	3.0	3.7 GB	For >=6GB VRAM cards
4.0bpw (main)	4.0	4.4 GB	For >=6GB VRAM cards
6.0bpw	6.0	6.1 GB	For >=8GB VRAM cards
8.0bpw	8.0	7.7 GB	For >=10GB VRAM cards

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Original Info

Merge Q-bert/MetaMath-Cybertron-Starling and fblgit/juanako-7b-UNA using slerp merge.

You can use ChatML format.

Open LLM Leaderboard Evaluation Results

Detailed results can be found Coming soon

Metric	Value
Avg.	Coming soon
ARC (25-shot)	Coming soon
HellaSwag (10-shot)	Coming soon
MMLU (5-shot)	Coming soon
TruthfulQA (0-shot)	Coming soon
Winogrande (5-shot)	Coming soon
GSM8K (5-shot)	Coming soon

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Merged-AGI-7B-EXL2