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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	---
	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](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 [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) as a base.

	### Models Merged

	The following models were included in the merge:
	* [bamec66557/VICIOUS_MESH-12B-BETA](https://huggingface.co/bamec66557/VICIOUS_MESH-12B-BETA)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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](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-NEMO-details)

	\| 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\|