Instructions to use lilmeaty/testing_semifinal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lilmeaty/testing_semifinal with Transformers:

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

pipe = pipeline("text-generation", model="lilmeaty/testing_semifinal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lilmeaty/testing_semifinal")
model = AutoModelForCausalLM.from_pretrained("lilmeaty/testing_semifinal", 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 lilmeaty/testing_semifinal with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lilmeaty/testing_semifinal

SGLang

How to use lilmeaty/testing_semifinal 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 "lilmeaty/testing_semifinal" \
    --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": "lilmeaty/testing_semifinal",
		"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 "lilmeaty/testing_semifinal" \
        --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": "lilmeaty/testing_semifinal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lilmeaty/testing_semifinal with Docker Model Runner:
```
docker model run hf.co/lilmeaty/testing_semifinal
```

testing_semifinal / mergekit_config.yml

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	models:
	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-qgcitfu
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-esawwda
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-dkhnzcn
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-poovzrh
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-pghuyfi
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-kmlzhzo
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: Hjgugugjhuhjggg/mergekit-ties-xflmond
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Long-Think
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Pure-RP
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Apex
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Mix-Skill
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Booval
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-ProdigyPlusPlus
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Prodigy
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Sci-Think
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Stock
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: chuanli11/Llama-3.2-3B-Instruct-uncensored
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: ValiantLabs/Llama3.2-3B-Enigma
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: CarrotAI/Llama-3.2-Rabbit-Ko-3B-Instruct
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: AELLM/Llama-3.2-Chibi-3B
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: EmTpro01/llama-3.2-Code-Generator
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: disi-unibo-nlp/llama3.2-3B-SFT-medmcqa-triples-cot
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: Atharva26/llama-3.2-3b-mathdaily-chatbot
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: Diluksha/Llama_3.2_3B_sql_finetuned_full
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-CodeReactor
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: AcademieDuNumerique/Llama-3.2-3B-SQL-Instruct
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: roger33303/Best_Model-llama3.2-3b-Instruct-Finetune-website-QnA
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: noaebbot/llama3.2-3B-insights
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-TitanFusion-v2
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-TitanFusion
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: bunnycore/Llama-3.2-3B-Mix
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: ValiantLabs/Llama3.2-3B-ShiningValiant2
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: TroyDoesAI/BlackSheep-Llama3.2-3B-Context_Obedient
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: BrainWave-ML/llama3.2-3B-codemath-orpo
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: CK0607/llama3.2-3B-CodeP
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: disi-unibo-nlp/llama3.2-3B-SFT-medqa-triples-cot
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: Isotonic/reasoning-llama3.2-3b
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: meta-llama/Llama-3.2-3B-Instruct
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	- layer_range: [0, 28]
	model: meta-llama/Llama-3.2-3B
	parameters:
	weight: 0.5
	density: 0.5
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100

	merge_method: linear
	base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	parameters:
	weight: 1
	density: 0.9
	gamma: 0.01
	normalize: true
	int8_mask: true
	random_seed: 0
	temperature: 0.5
	top_p: 0.65
	inference: true
	max_tokens: 999999999
	stream: true
	quantization:
	- method: int8
	value: 100
	- method: int4
	value: 100
	dtype: float16