Instructions to use ConicCat/Qwriter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ConicCat/Qwriter2 with Transformers:

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

pipe = pipeline("text-generation", model="ConicCat/Qwriter2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ConicCat/Qwriter2")
model = AutoModelForCausalLM.from_pretrained("ConicCat/Qwriter2")
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

vLLM

How to use ConicCat/Qwriter2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ConicCat/Qwriter2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConicCat/Qwriter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ConicCat/Qwriter2

SGLang

How to use ConicCat/Qwriter2 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 "ConicCat/Qwriter2" \
    --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": "ConicCat/Qwriter2",
		"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 "ConicCat/Qwriter2" \
        --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": "ConicCat/Qwriter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ConicCat/Qwriter2 with Docker Model Runner:
```
docker model run hf.co/ConicCat/Qwriter2
```

Qwriter2 / README.md

ConicCat

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metadata

base_model:
  - Qwen/Qwen2.5-32B
  - THU-KEG/LongWriter-Zero-32B
  - Qwen/QwQ-32B
library_name: transformers
tags:
  - mergekit
  - merge

Qwriter

Cursed merge attempt number 3

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: THU-KEG/LongWriter-Zero-32B
    parameters:
      weight: 0.5
  - model: Qwen/QwQ-32B
    parameters:
      weight: 0.5
merge_method: della
base_model: Qwen/Qwen2.5-32B
dtype: bfloat16
tokenizer:
  source: union
  tokens:
    # Use embedding from a specific model
    <|im_start|>:
      source: "Qwen/QwQ-32B"
    <|im_end|>:
      source: "Qwen/QwQ-32B"