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

Libraries

How to use Azazelle/SlimMelodicMaid with Transformers:

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

pipe = pipeline("text-generation", model="Azazelle/SlimMelodicMaid")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/SlimMelodicMaid")
model = AutoModelForCausalLM.from_pretrained("Azazelle/SlimMelodicMaid")

Inference
Local Apps Settings

vLLM

How to use Azazelle/SlimMelodicMaid with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Azazelle/SlimMelodicMaid

SGLang

How to use Azazelle/SlimMelodicMaid 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 "Azazelle/SlimMelodicMaid" \
    --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": "Azazelle/SlimMelodicMaid",
		"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 "Azazelle/SlimMelodicMaid" \
        --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": "Azazelle/SlimMelodicMaid",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Model Card for xDAN-SlimOrca

Slerp merge of Silicon-Maid-7B, piano-medley-7b, xDAN-L1-Chat-RL-v1, and mistral-7b-slimorcaboros.

.yaml file for mergekit

slices:
  - sources:
      - model: Azazelle/Silicon-Medley
        layer_range: [0, 32]
      - model: Azazelle/xDAN-SlimOrca
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0.19, 0.59, 0.43, 0.76, 1]
    - filter: mlp
      value: [0.81, 0.41, 0.57, 0.24, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.70
AI2 Reasoning Challenge (25-Shot)	67.15
HellaSwag (10-Shot)	86.01
MMLU (5-Shot)	64.75
TruthfulQA (0-shot)	60.88
Winogrande (5-shot)	78.61
GSM8k (5-shot)	60.80

Downloads last month: 113

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Azazelle/SlimMelodicMaid

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

67.150
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.010
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.750
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

60.880
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.610
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

60.800