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
```

modelo_fusionado

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

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

Downloads last month: 16

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for edusc182/Qwen2.5-Coder-3B-Web-Creator-SLERP

edusc182/Qwen2.5-Coder-Web-Creator-Tailwin-Abliterated

edusc182/Qwen2.5-Coder_Web_Creator_Tailwin-Abliteratedv2

Merge model

this model