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Instructions to use Undi95/FlatDolphinMaid-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/FlatDolphinMaid-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/FlatDolphinMaid-8x7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/FlatDolphinMaid-8x7B")
model = AutoModelForCausalLM.from_pretrained("Undi95/FlatDolphinMaid-8x7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Undi95/FlatDolphinMaid-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/FlatDolphinMaid-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/FlatDolphinMaid-8x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/FlatDolphinMaid-8x7B

SGLang

How to use Undi95/FlatDolphinMaid-8x7B 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 "Undi95/FlatDolphinMaid-8x7B" \
    --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": "Undi95/FlatDolphinMaid-8x7B",
		"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 "Undi95/FlatDolphinMaid-8x7B" \
        --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": "Undi95/FlatDolphinMaid-8x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/FlatDolphinMaid-8x7B with Docker Model Runner:
```
docker model run hf.co/Undi95/FlatDolphinMaid-8x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

First experimental merge of Noromaid 8x7b (Instruct) and dolphin 8x7b. The idea behind this is to add a little more IQ to the model, because Noromaid was only trained on RP/ERP data. Dolphin 2.7 is the only real Mixtral finetune I consider "usable", and so the merging quest begin again kek.

Merged Dolphin 2.7 with Mixtral Base (Dolphin was at 1.0 weight) to get rid of ChatLM, and then I merged Noromaid 8x7b with the output, SLERP method.

This model feel better on the IQ chart and have the ~same average ERP score on ayumi bench' than Noromaid 8x7b, but it's softer and more prude too, it also have the typical Mixtral repeat issue at some point. Choose your poison.

Description

This repo contains fp16 files of FlatDolphinMaid-8x7B.

Models used

Custom format:

### Instruction:
{system prompt}

### Input:
{input}

### Response:
{reply}

If you want to support me, you can here.

Downloads last month: 10

Safetensors

Model size

47B params

Tensor type

BF16

Model tree for Undi95/FlatDolphinMaid-8x7B

Quantizations

6 models