Instructions to use solidrust/MistarlingMaid-2x7B-base-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/MistarlingMaid-2x7B-base-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/MistarlingMaid-2x7B-base-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/MistarlingMaid-2x7B-base-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/MistarlingMaid-2x7B-base-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/MistarlingMaid-2x7B-base-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/MistarlingMaid-2x7B-base-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/MistarlingMaid-2x7B-base-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/MistarlingMaid-2x7B-base-AWQ

SGLang

How to use solidrust/MistarlingMaid-2x7B-base-AWQ 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 "solidrust/MistarlingMaid-2x7B-base-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/MistarlingMaid-2x7B-base-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "solidrust/MistarlingMaid-2x7B-base-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/MistarlingMaid-2x7B-base-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/MistarlingMaid-2x7B-base-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/MistarlingMaid-2x7B-base-AWQ
```

dawn17/MistarlingMaid-2x7B-base AWQ

Model creator: dawn17
Original model: MistarlingMaid-2x7B-base

Model Summary

    base_model: dawn17/MistarlingMaid-2x7B-base
    gate_mode: hidden # one of "hidden", "cheap_embed", or "random"
    dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
    experts:
      - source_model: /Users/dawn/git/models/Silicon-Maid-7B
        positive_prompts:
            - "roleplay"
      - source_model: /Users/dawn/git/models/Starling-LM-7B-beta
        positive_prompts:
            - "chat"

Open LLM Leaderboard Evaluation Results

Metric	Value
Avg.	68.01
AI2 Reasoning Challenge (25-Shot)	67.49
HellaSwag (10-Shot)	84.76
MMLU (5-Shot)	62.62
TruthfulQA (0-shot)	58.93
Winogrande (5-shot)	78.22
GSM8k (5-shot)	56.03

Downloads last month: 4

Safetensors

Model size

13B params

Tensor type

I32

F16

Model tree for solidrust/MistarlingMaid-2x7B-base-AWQ

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dawn17/MistarlingMaid-2x7B-base

Quantized

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Collection including solidrust/MistarlingMaid-2x7B-base-AWQ

2x7B AWQ

Collection

Mixture of experts 2 x 7B. • 20 items • Updated Apr 30, 2024