Instructions to use maldv/spring-chicken-8x8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/spring-chicken-8x8b with Transformers:

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

pipe = pipeline("text-generation", model="maldv/spring-chicken-8x8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/spring-chicken-8x8b")
model = AutoModelForCausalLM.from_pretrained("maldv/spring-chicken-8x8b")
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 maldv/spring-chicken-8x8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/spring-chicken-8x8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/spring-chicken-8x8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/spring-chicken-8x8b

SGLang

How to use maldv/spring-chicken-8x8b 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 "maldv/spring-chicken-8x8b" \
    --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": "maldv/spring-chicken-8x8b",
		"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 "maldv/spring-chicken-8x8b" \
        --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": "maldv/spring-chicken-8x8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maldv/spring-chicken-8x8b with Docker Model Runner:
```
docker model run hf.co/maldv/spring-chicken-8x8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GGUF Quants

Spring Chicken 8x8b

I've been really impressed with how well these frankenmoe models quant compared to the base llama 8b, but with far better speed than the 70b. There have been some great 4x8b models released recently, so I tried an 8x8b.

base_model: ./maldv/spring
gate_mode: hidden 
dtype: bfloat16
experts_per_token: 2
experts:
  - source_model: ./models/Llama3-ChatQA-1.5-8B
    positive_prompts:
      - 'add numbers'
      - 'solve for x'
    negative_prompts:
      - 'I love you'
      - 'Help me'
  - source_model: ./models/InfinityRP-v2-8B
    positive_prompts:
    - 'they said'
  - source_model: ./models/Einstein-v6.1-Llama3-8B
    positive_prompts:
    - 'the speed of light'
    - 'chemical reaction'
  - source_model: ./models/Llama-3-Soliloquy-8B-v2
    positive_prompts:
    - 'write a'
  - source_model: ./models/Llama-3-Lumimaid-8B-v0.1
    positive_prompts:
    - 'she looked'
  - source_model: ./models/L3-TheSpice-8b-v0.8.3
    positive_prompts:
    - 'they felt'
  - source_model: ./models/Llama3-OpenBioLLM-8B
    positive_prompts:
    - 'the correct treatment'
  - source_model: ./models/Llama-3-SauerkrautLM-8b-Instruct
    positive_prompts:
    - 'help me'
    - 'should i'

Spring

Spring is a cascading dare-ties merge of the following models:

[
  'Einstein-v6.1-Llama3-8B',
  'L3-TheSpice-8b-v0.8.3',
  'Configurable-Hermes-2-Pro-Llama-3-8B',
  'Llama3-ChatQA-1.5-8B',
  'Llama3-OpenBioLLM-8B',
  'InfinityRP-v2-8B',
  'Llama-3-Soliloquy-8B-v2',
  'Tiamat-8b-1.2-Llama-3-DPO',
  'Llama-3-8B-Instruct-Gradient-1048k',
  'Llama-3-Lumimaid-8B-v0.1',
  'Llama-3-SauerkrautLM-8b-Instruct',
  'Meta-Llama-3-8B-Instruct-DPO',
]

I'm finding my iq4_xs to be working well. Llama 3 instruct format works well, but minimal format is highly creative.

Scores

Not greater than the sum of it's parts, based on the scores; but it is really smart for an emotive RP model.

Metric	Score
Average	65.89
ARC	63.05
HellaSwag	82.49
MMLU	64.45
TruthfulQA	51.63
Winogrande	76.24
GSM8K	51.63

Details

Downloads last month: 14

Safetensors

Model size

47B params

Tensor type

BF16

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Quantizations

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