Instructions to use sequelbox/Qwen3-14B-Esper3Grayline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sequelbox/Qwen3-14B-Esper3Grayline with Transformers:

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

pipe = pipeline("text-generation", model="sequelbox/Qwen3-14B-Esper3Grayline")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Qwen3-14B-Esper3Grayline")
model = AutoModelForCausalLM.from_pretrained("sequelbox/Qwen3-14B-Esper3Grayline", 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 sequelbox/Qwen3-14B-Esper3Grayline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sequelbox/Qwen3-14B-Esper3Grayline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sequelbox/Qwen3-14B-Esper3Grayline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sequelbox/Qwen3-14B-Esper3Grayline

SGLang

How to use sequelbox/Qwen3-14B-Esper3Grayline 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 "sequelbox/Qwen3-14B-Esper3Grayline" \
    --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": "sequelbox/Qwen3-14B-Esper3Grayline",
		"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 "sequelbox/Qwen3-14B-Esper3Grayline" \
        --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": "sequelbox/Qwen3-14B-Esper3Grayline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sequelbox/Qwen3-14B-Esper3Grayline with Docker Model Runner:
```
docker model run hf.co/sequelbox/Qwen3-14B-Esper3Grayline
```

Qwen3-14B-Esper3Grayline / README.md

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metadata

base_model:
  - Qwen/Qwen3-14B
  - ValiantLabs/Qwen3-14B-Esper3
  - soob3123/GrayLine-Qwen3-14B
library_name: transformers
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - mergekit
  - merge
  - esper
  - esper-3
  - grayline
  - valiant
  - valiant-labs
  - qwen
  - qwen-3
  - qwen-3-14b
  - 14b
  - reasoning
  - code
  - code-instruct
  - python
  - javascript
  - dev-ops
  - jenkins
  - terraform
  - scripting
  - powershell
  - azure
  - aws
  - gcp
  - cloud
  - problem-solving
  - architect
  - engineer
  - developer
  - creative
  - analytical
  - expert
  - rationality
  - uncensored
  - unfiltered
  - amoral-ai
  - conversational
  - chat
  - instruct

sequelbox/Qwen3-14B-Esper3Grayline

This is a merge of pre-trained language models created using mergekit, combining Esper 3 14b's specialty skills with Grayline 14b's uncensored reasoning.

Merge Details

Merge Method

This model was merged using the DELLA merge method using Qwen/Qwen3-14B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: della
dtype: bfloat16
parameters:
  normalize: true
models:
  - model: ValiantLabs/Qwen3-14B-Esper3
    parameters:
      density: 0.5
      weight: 0.3
  - model: soob3123/GrayLine-Qwen3-14B
    parameters:
      density: 0.5
      weight: 0.25
base_model: Qwen/Qwen3-14B