Instructions to use bunnycore/Llama-3.1-8B-TitanFusion-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Llama-3.1-8B-TitanFusion-v3 with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Llama-3.1-8B-TitanFusion-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Llama-3.1-8B-TitanFusion-v3")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Llama-3.1-8B-TitanFusion-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bunnycore/Llama-3.1-8B-TitanFusion-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunnycore/Llama-3.1-8B-TitanFusion-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunnycore/Llama-3.1-8B-TitanFusion-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bunnycore/Llama-3.1-8B-TitanFusion-v3

SGLang

How to use bunnycore/Llama-3.1-8B-TitanFusion-v3 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 "bunnycore/Llama-3.1-8B-TitanFusion-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunnycore/Llama-3.1-8B-TitanFusion-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "bunnycore/Llama-3.1-8B-TitanFusion-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunnycore/Llama-3.1-8B-TitanFusion-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bunnycore/Llama-3.1-8B-TitanFusion-v3 with Docker Model Runner:
```
docker model run hf.co/bunnycore/Llama-3.1-8B-TitanFusion-v3
```

Llama-3.1-8B-TitanFusion-v3 / README.md

bunnycore

Adding Evaluation Results (#1)

e3d12f1 verified almost 2 years ago

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	---
	library_name: transformers
	tags:
	- mergekit
	- merge
	base_model:
	- DreadPoor/Heart_Stolen-8B-Model_Stock
	- bunnycore/Llama-3.1-8B-TitanFusion
	- arcee-ai/Llama-3.1-SuperNova-Lite
	- DreadPoor/Aspire-8B-model_stock
	- vicgalle/Configurable-Llama-3.1-8B-Instruct
	- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
	model-index:
	- name: Llama-3.1-8B-TitanFusion-v3
	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: 48.1
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	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: 32.07
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	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: 12.99
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	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: 7.83
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	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: 11.94
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	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: 31.17
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Llama-3.1-8B-TitanFusion-v3
	name: Open LLM Leaderboard
	---
	# 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [bunnycore/Llama-3.1-8B-TitanFusion](https://huggingface.co/bunnycore/Llama-3.1-8B-TitanFusion) as a base.

	### Models Merged

	The following models were included in the merge:
	* [DreadPoor/Heart_Stolen-8B-Model_Stock](https://huggingface.co/DreadPoor/Heart_Stolen-8B-Model_Stock)
	* [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite)
	* [DreadPoor/Aspire-8B-model_stock](https://huggingface.co/DreadPoor/Aspire-8B-model_stock)
	* [vicgalle/Configurable-Llama-3.1-8B-Instruct](https://huggingface.co/vicgalle/Configurable-Llama-3.1-8B-Instruct)
	* [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: DreadPoor/Aspire-8B-model_stock
	- model: arcee-ai/Llama-3.1-SuperNova-Lite
	- model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
	- model: bunnycore/Llama-3.1-8B-TitanFusion
	- model: vicgalle/Configurable-Llama-3.1-8B-Instruct
	- model: DreadPoor/Heart_Stolen-8B-Model_Stock
	merge_method: model_stock
	base_model: bunnycore/Llama-3.1-8B-TitanFusion
	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/details_bunnycore__Llama-3.1-8B-TitanFusion-v3)

	\| Metric \|Value\|
	\|-------------------\|----:\|
	\|Avg. \|24.02\|
	\|IFEval (0-Shot) \|48.10\|
	\|BBH (3-Shot) \|32.07\|
	\|MATH Lvl 5 (4-Shot)\|12.99\|
	\|GPQA (0-shot) \| 7.83\|
	\|MuSR (0-shot) \|11.94\|
	\|MMLU-PRO (5-shot) \|31.17\|