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

Libraries

How to use Azazelle/xDAN-SlimOrca with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/xDAN-SlimOrca")
model = AutoModelForCausalLM.from_pretrained("Azazelle/xDAN-SlimOrca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azazelle/xDAN-SlimOrca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Azazelle/xDAN-SlimOrca

SGLang

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

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

Model Card for xDAN-SlimOrca

Slerp merge of xDAN-L1-Chat-RL-v1 and mistral-7b-slimorcaboros.

.yaml file for mergekit

slices:
  - sources:
      - model: xDAN-AI/xDAN-L1-Chat-RL-v1
        layer_range: [0, 32]
      - model: openaccess-ai-collective/mistral-7b-slimorcaboros
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0.14, 0.57, 0.4, 0.74, 1]
    - filter: mlp
      value: [0.86, 0.43, 0.6, 0.26, 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.	68.04
AI2 Reasoning Challenge (25-Shot)	65.61
HellaSwag (10-Shot)	85.70
MMLU (5-Shot)	63.67
TruthfulQA (0-shot)	57.68
Winogrande (5-shot)	77.66
GSM8k (5-shot)	57.92

Downloads last month: 229

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Azazelle/xDAN-SlimOrca

Quantizations

1 model

Evaluation results

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

65.610
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.700
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.670
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

57.680
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.660
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

57.920