Instructions to use edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP with Transformers:

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

pipe = pipeline("text-generation", model="edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP")
model = AutoModelForCausalLM.from_pretrained("edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP

SGLang

How to use edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP 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 "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP" \
    --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": "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP",
		"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 "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP" \
        --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": "edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP with Docker Model Runner:
```
docker model run hf.co/edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP
```

Qwen2.5-Coder-3B-Web-Creator-SLERP / README.md

edusc182

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	---
	base_model:
	- edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated
	- edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# modelo_fusionado

	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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

	### Models Merged

	The following models were included in the merge:
	* [edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated](https://huggingface.co/edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated)
	* [edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2](https://huggingface.co/edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	slices:
	- sources:
	- model: edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated
	layer_range: [0, 36]
	- model: edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2
	layer_range: [0, 36]
	merge_method: slerp
	base_model: edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.5, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.5, 0.7, 0.3, 0]
	- value: 0.5
	dtype: bfloat16
	```