Not-For-All-Audiences

Instructions to use bamec66557/VICIOUS_MESH-12B-NEMO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bamec66557/VICIOUS_MESH-12B-NEMO with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bamec66557/VICIOUS_MESH-12B-NEMO")
model = AutoModelForCausalLM.from_pretrained("bamec66557/VICIOUS_MESH-12B-NEMO", 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/VICIOUS_MESH-12B-NEMO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bamec66557/VICIOUS_MESH-12B-NEMO"
# 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/VICIOUS_MESH-12B-NEMO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bamec66557/VICIOUS_MESH-12B-NEMO

SGLang

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

Docker Model Runner
How to use bamec66557/VICIOUS_MESH-12B-NEMO with Docker Model Runner:
```
docker model run hf.co/bamec66557/VICIOUS_MESH-12B-NEMO
```

VICIOUS_MESH-12B-NEMO / README.md

bamec66557

Adding Evaluation Results (#1)

8f499ce verified over 1 year ago

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metadata

license: apache-2.0
library_name: transformers
tags:
  - mergekit
  - merge
  - not-for-all-audiences
base_model:
  - mistralai/Mistral-Nemo-Instruct-2407
model-index:
  - name: VICIOUS_MESH-12B-NEMO
    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: 40.22
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          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: 34.4
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          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: 11.71
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          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: 9.84
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          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: 12.57
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          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: 30.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-NEMO
          name: Open LLM Leaderboard

VICIOUS_MESH-12B-NEMO

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

Merge Details

Merge Method

This model was merged using the TIES merge method using mistralai/Mistral-Nemo-Instruct-2407 as a base.

Models Merged

The following models were included in the merge:

bamec66557/VICIOUS_MESH-12B-BETA

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Mistral-Nemo-Instruct-2407
    #no parameters necessary for base model 
  - model: VICIOUS_MESH-12B-BETA
    parameters:
      density: 1
      weight: 1
merge_method: ties
base_model: Mistral-Nemo-Instruct-2407
tokenizer_source: base
chat_template: "auto"
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16
regularization:
  - method: weight_clipping
    clip_range: [-0.1, 0.1]
  - method: l2_norm
    scale: 0.002
# Postprocessing to refine the merged model
postprocessing:
  - operation: dynamic_scaling
    scale_range: [0.9, 1.1]
  - operation: gaussian_smoothing
    sigma: 1.0
  - operation: normalize

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	23.15
IFEval (0-Shot)	40.22
BBH (3-Shot)	34.40
MATH Lvl 5 (4-Shot)	11.71
GPQA (0-shot)	9.84
MuSR (0-shot)	12.57
MMLU-PRO (5-shot)	30.18