Instructions to use top-50000/testing-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use top-50000/testing-2 with Transformers:

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

pipe = pipeline("text-generation", model="top-50000/testing-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("top-50000/testing-2")
model = AutoModelForCausalLM.from_pretrained("top-50000/testing-2")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use top-50000/testing-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "top-50000/testing-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "top-50000/testing-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/top-50000/testing-2

SGLang

How to use top-50000/testing-2 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 "top-50000/testing-2" \
    --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": "top-50000/testing-2",
		"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 "top-50000/testing-2" \
        --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": "top-50000/testing-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use top-50000/testing-2 with Docker Model Runner:
```
docker model run hf.co/top-50000/testing-2
```

testing-2 / README.md

top-50000

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	---
	base_model: []
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merged_4b

	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 ./evolve_storage/input_models/Qwen3-4B_131373129 as a base.

	### Models Merged

	The following models were included in the merge:
	* ./evolve_storage/input_models/4b_1_2932744254
	* ./evolve_storage/input_models/4b_2_935817604

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: ./evolve_storage/input_models/Qwen3-4B_131373129
	dtype: bfloat16
	merge_method: ties
	parameters:
	int8_mask: 1.0
	normalize: 1.0
	slices:
	- sources:
	- layer_range: [0, 6]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 1.0
	- value: 0.8608594704318231
	weight:
	- filter: mlp
	value: 0.3490062747244206
	- filter: self_attn
	value: 0.09060462688545917
	- value: 0.7576223803340598
	- layer_range: [0, 6]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 0.9307034739117228
	- filter: self_attn
	value: 0.7266676131372826
	- value: 0.7992834608507406
	weight:
	- filter: mlp
	value: 0.02382702396781508
	- filter: self_attn
	value: 0.19373265930727943
	- value: 0.5095495553391711
	- layer_range: [0, 6]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129
	- sources:
	- layer_range: [6, 12]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 0.9956934876179888
	- value: 1.0
	weight:
	- filter: mlp
	value: 0.7637369917121993
	- filter: self_attn
	value: 0.7579899664980693
	- value: 0.369874736821347
	- layer_range: [6, 12]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 0.7931662945197395
	- value: 1.0
	weight:
	- filter: mlp
	value: 0.05065405710236617
	- filter: self_attn
	value: 0.1681017405390699
	- value: 0.278174377018312
	- layer_range: [6, 12]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129
	- sources:
	- layer_range: [12, 18]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 1.0
	- value: 1.0
	weight:
	- filter: mlp
	value: 0.1896079785687373
	- filter: self_attn
	value: 0.28109871222289246
	- value: 0.5919682098365076
	- layer_range: [12, 18]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 0.9657611673995403
	- filter: self_attn
	value: 0.7203176611210709
	- value: 0.5429222666986293
	weight:
	- filter: mlp
	value: 0.6105068874861986
	- filter: self_attn
	value: 0.35883124159464513
	- value: 0.08904009475761779
	- layer_range: [12, 18]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129
	- sources:
	- layer_range: [18, 24]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 0.9149303352862976
	- filter: self_attn
	value: 1.0
	- value: 0.877602461891939
	weight:
	- filter: mlp
	value: 0.4686086798952363
	- filter: self_attn
	value: 0.9038594427840757
	- value: 0.10082926037736678
	- layer_range: [18, 24]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 0.7101112464063535
	- filter: self_attn
	value: 0.7817731468186313
	- value: 0.39642301784045736
	weight:
	- filter: mlp
	value: 0.12881047196006434
	- filter: self_attn
	value: 0.5913495175648413
	- value: 0.4608092365074321
	- layer_range: [18, 24]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129
	- sources:
	- layer_range: [24, 30]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 0.5207504504970493
	- filter: self_attn
	value: 0.7016905835163534
	- value: 0.7841906098086426
	weight:
	- filter: mlp
	value: 0.03936529169370809
	- filter: self_attn
	value: 0.3540197424712478
	- value: 0.23794897877129362
	- layer_range: [24, 30]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 0.6658594462559264
	- filter: self_attn
	value: 1.0
	- value: 0.7557856184584113
	weight:
	- filter: mlp
	value: 0.5961021443524465
	- filter: self_attn
	value: 0.5159043688030769
	- value: 0.7605556292871718
	- layer_range: [24, 30]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129
	- sources:
	- layer_range: [30, 36]
	model: ./evolve_storage/input_models/4b_1_2932744254
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 1.0
	- value: 0.63120667056331
	weight:
	- filter: mlp
	value: 0.15069904386157829
	- filter: self_attn
	value: 0.6308542464264801
	- value: 0.4804838636683506
	- layer_range: [30, 36]
	model: ./evolve_storage/input_models/4b_2_935817604
	parameters:
	density:
	- filter: mlp
	value: 1.0
	- filter: self_attn
	value: 1.0
	- value: 0.924178196118008
	weight:
	- filter: mlp
	value: 0.5716878395339086
	- filter: self_attn
	value: 0.19598590068808175
	- value: 0.8797652412255632
	- layer_range: [30, 36]
	model: ./evolve_storage/input_models/Qwen3-4B_131373129

	```