Not-For-All-Audiences

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

Libraries

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

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

pipe = pipeline("text-generation", model="bamec66557/VICIOUS_MESH-12B-BETA")
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-BETA")
model = AutoModelForCausalLM.from_pretrained("bamec66557/VICIOUS_MESH-12B-BETA", 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-BETA 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-BETA"
# 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-BETA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

VICIOUS_MESH-12B-BETA

File size: 4,914 Bytes

---
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- not-for-all-audiences
base_model:
- bamec66557/VICIOUS_MESH-12B-ALPHA
- Infermatic/MN-12B-Inferor-v0.1
- Khetterman/DarkAtom-12B-v3
model-index:
- name: VICIOUS_MESH-12B-BETA
  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: 67.21
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      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: 31.36
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      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: 12.08
      name: exact match
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      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: 8.84
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      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: 14.34
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      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: 29.76
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/VICIOUS_MESH-12B-BETA
      name: Open LLM Leaderboard
datasets:
- open-llm-leaderboard/bamec66557__VICIOUS_MESH-12B-BETA-details
- open-llm-leaderboard/bamec66557__VICIOUS_MESH-12B-ALPHA-details
language:
- en
- ja
- ru
- zh
- ko
- es
---
<a href="#" target="_blank"><img src="https://huggingface.co/bamec66557/MISCHIEVOUS-12B/resolve/main/00001-321918068.gif"></a>


# [GGUF] 

* [ Q4_K_M.gguf ](https://huggingface.co/bamec66557/VICIOUS_MESH-12B-BETA-Q4_K_M-GGUF)


# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Khetterman/DarkAtom-12B-v3](https://huggingface.co/Khetterman/DarkAtom-12B-v3) as a base.

### Models Merged

The following models were included in the merge:
* [bamec66557/VICIOUS_MESH-12B-ALPHA](https://huggingface.co/bamec66557/VICIOUS_MESH-12B-ALPHA)
* [Infermatic/MN-12B-Inferor-v0.1](https://huggingface.co/Infermatic/MN-12B-Inferor-v0.1)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: Khetterman/DarkAtom-12B-v3
    #no parameters necessary for base model
  - model: Infermatic/MN-12B-Inferor-v0.1
    parameters:
      density: 0.5
      weight: 0.5
  - model: bamec66557/VICIOUS_MESH-12B-ALPHA
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Khetterman/DarkAtom-12B-v3
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/bamec66557__VICIOUS_MESH-12B-BETA-details)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |27.26|
|IFEval (0-Shot)    |67.21|
|BBH (3-Shot)       |31.36|
|MATH Lvl 5 (4-Shot)|12.08|
|GPQA (0-shot)      | 8.84|
|MuSR (0-shot)      |14.34|
|MMLU-PRO (5-shot)  |29.76|