Not-For-All-Audiences

Instructions to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with Transformers:

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

pipe = pipeline("text-generation", model="bamec66557/MISCHIEVOUS-12B-Mix_0.1v")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bamec66557/MISCHIEVOUS-12B-Mix_0.1v")
model = AutoModelForCausalLM.from_pretrained("bamec66557/MISCHIEVOUS-12B-Mix_0.1v", device_map="auto")
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 bamec66557/MISCHIEVOUS-12B-Mix_0.1v with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bamec66557/MISCHIEVOUS-12B-Mix_0.1v"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bamec66557/MISCHIEVOUS-12B-Mix_0.1v

SGLang

How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v 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 "bamec66557/MISCHIEVOUS-12B-Mix_0.1v" \
    --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": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v",
		"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 "bamec66557/MISCHIEVOUS-12B-Mix_0.1v" \
        --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": "bamec66557/MISCHIEVOUS-12B-Mix_0.1v",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bamec66557/MISCHIEVOUS-12B-Mix_0.1v with Docker Model Runner:
```
docker model run hf.co/bamec66557/MISCHIEVOUS-12B-Mix_0.1v
```

MISCHIEVOUS-12B-Mix_0.1v / README.md

bamec66557

Adding Evaluation Results (#1)

c665f50 verified over 1 year ago

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metadata

language:
  - en
  - ko
license: apache-2.0
library_name: transformers
tags:
  - mergekit
  - merge
  - text-generation-inference
  - not-for-all-audiences
base_model:
  - bamec66557/MNRP_0.5
  - bamec66557/MISCHIEVOUS-12B
model-index:
  - name: MISCHIEVOUS-12B-Mix_0.1v
    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: 36.36
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          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: 34.36
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          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: 12.76
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          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: 10.4
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          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: 11.54
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          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: 29.71
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
          name: Open LLM Leaderboard

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

bamec66557/MNRP_0.5
bamec66557/MISCHIEVOUS-12B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - Sources:
      - model: bamec66557/MNRP_0.5
        layer_range: [0, 40] # Merge layer range for MNRP_0.5 model
      - model: bamec66557/MISCHIEVOUS-12B
        layer_range: [0, 40] # Merge layer range for MISCHIEVOUS-12B model.

    # Adjust the merge ratio per layer to drive smoother integration
    # Each filter affects a specific mechanism within the model
    parameters:
      t:
        - Filter: self_attn
          value: [0.2, 0.4, 0.6, 0.8, 1.0] # Progressive merging of self-attention layers
        - filter: mlp
          value: [0.8, 0.6, 0.4, 0.2, 0.0] # Merge MLP layers with opposite proportions
        - filter: layer_norm
          value: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # Layer Normalisation should be merged uniformly
        - value: 0.7 # Default

merge_method: slerp # change merge method to slerp

base_model: bamec66557/MISCHIEVOUS-12B # base model for merge

dtype: bfloat16 # data type for efficient and fast operations when merging

# Additional available options
regularisation:
  - method: l2_norm # Stabilise merged model weights with L2 normalisation
    scale: 0.01

postprocessing:
  - operation: smoothing # Smooth the weights after merging
    kernel_size: 3
  - operation: normalise # normalise the overall weights

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	22.52
IFEval (0-Shot)	36.36
BBH (3-Shot)	34.36
MATH Lvl 5 (4-Shot)	12.76
GPQA (0-shot)	10.40
MuSR (0-shot)	11.54
MMLU-PRO (5-shot)	29.71