Text Generation
Transformers
Safetensors
qwen3
mergekit
Merge
conversational
text-generation-inference
Instructions to use top-50000/testing-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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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
```
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