Not-For-All-Audiences

Instructions to use ND911/Eclectic-Maid-10B-v2-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ND911/Eclectic-Maid-10B-v2-1 with Transformers:

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

pipe = pipeline("text-generation", model="ND911/Eclectic-Maid-10B-v2-1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ND911/Eclectic-Maid-10B-v2-1")
model = AutoModelForCausalLM.from_pretrained("ND911/Eclectic-Maid-10B-v2-1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ND911/Eclectic-Maid-10B-v2-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ND911/Eclectic-Maid-10B-v2-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ND911/Eclectic-Maid-10B-v2-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ND911/Eclectic-Maid-10B-v2-1

SGLang

How to use ND911/Eclectic-Maid-10B-v2-1 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 "ND911/Eclectic-Maid-10B-v2-1" \
    --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": "ND911/Eclectic-Maid-10B-v2-1",
		"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 "ND911/Eclectic-Maid-10B-v2-1" \
        --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": "ND911/Eclectic-Maid-10B-v2-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ND911/Eclectic-Maid-10B-v2-1 with Docker Model Runner:
```
docker model run hf.co/ND911/Eclectic-Maid-10B-v2-1
```

Eclectic-Maid-10B-v2-1

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---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
- not-for-all-audiences
---

![](eemaid.png)


---
# Eclectic-Maid-10B-v2-1

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details

A much better improvement over Eclectic-Maid-10B-v2

### Merge Method

This model was merged using the passthrough merge method.

### Models Merged

The following models were included in the merge:
* Eclectic-Maid-v2

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
    - model: Eclectic-Maid-v2
      layer_range: [0, 16]
  - sources:
    - model: Eclectic-Maid-v2
      layer_range: [8, 24]
  - sources:
    - model: Eclectic-Maid-v2
      layer_range: [17, 32]
merge_method: passthrough
dtype: float16
name: Eclectic-Maid-v2
```