Instructions to use moeru-ai/L3.1-Moe-4x8B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moeru-ai/L3.1-Moe-4x8B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="moeru-ai/L3.1-Moe-4x8B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moeru-ai/L3.1-Moe-4x8B-v0.2")
model = AutoModelForCausalLM.from_pretrained("moeru-ai/L3.1-Moe-4x8B-v0.2")
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 Settings

vLLM

How to use moeru-ai/L3.1-Moe-4x8B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moeru-ai/L3.1-Moe-4x8B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moeru-ai/L3.1-Moe-4x8B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moeru-ai/L3.1-Moe-4x8B-v0.2

SGLang

How to use moeru-ai/L3.1-Moe-4x8B-v0.2 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 "moeru-ai/L3.1-Moe-4x8B-v0.2" \
    --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": "moeru-ai/L3.1-Moe-4x8B-v0.2",
		"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 "moeru-ai/L3.1-Moe-4x8B-v0.2" \
        --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": "moeru-ai/L3.1-Moe-4x8B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moeru-ai/L3.1-Moe-4x8B-v0.2 with Docker Model Runner:
```
docker model run hf.co/moeru-ai/L3.1-Moe-4x8B-v0.2
```

L3.1-Moe-4x8B-v0.2 / README.md

kwaabot

Adding Evaluation Results (#1)

8ebfacc verified over 1 year ago

preview code

raw

history blame contribute delete

5.43 kB

metadata

license: llama3.1
library_name: transformers
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
base_model:
  - Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
  - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
  - rombodawg/rombos_Replete-Coder-Instruct-8b-Merged
  - 3rd-Degree-Burn/Llama-3.1-8B-Squareroot-v0
model-index:
  - name: L3.1-Moe-4x8B-v0.2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 54.07
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 21.34
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 5.29
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 2.24
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 2.29
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 19.58
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=moeru-ai/L3.1-Moe-4x8B-v0.2
          name: Open LLM Leaderboard

L3.1-Moe-4x8B-v0.2

This model is a Mixture of Experts (MoE) made with mergekit-moe. It uses the following base models:

Heavily inspired by mlabonne/Beyonder-4x7B-v3.

Quantized models

GGUF by mradermacher

Mergekit config

base_model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
    positive_prompts: &chat_prompts
      - "chat"
      - "assistant"
      - "tell me"
      - "explain"
      - "I want"
    negative_prompts: &rp_prompts
      - "storywriting"
      - "write"
      - "scene"
      - "story"
      - "character"
  - source_model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2
    positive_prompts: *rp_prompts
    negative_prompts: *chat_prompts
  - source_model: rombodawg/rombos_Replete-Coder-Instruct-8b-Merged
    positive_prompts:
      - "code"
      - "python"
      - "javascript"
      - "programming"
      - "algorithm"
  - source_model: 3rd-Degree-Burn/Llama-3.1-8B-Squareroot-v0
    positive_prompts:
      - "reason"
      - "math"
      - "mathematics"
      - "solve"
      - "count"

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	17.47
IFEval (0-Shot)	54.07
BBH (3-Shot)	21.34
MATH Lvl 5 (4-Shot)	5.29
GPQA (0-shot)	2.24
MuSR (0-shot)	2.29
MMLU-PRO (5-shot)	19.58