Instructions to use staeiou/bartleby-dlo-qwen3.5-2b-base-cpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use staeiou/bartleby-dlo-qwen3.5-2b-base-cpt with Transformers:

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

pipe = pipeline("text-generation", model="staeiou/bartleby-dlo-qwen3.5-2b-base-cpt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("staeiou/bartleby-dlo-qwen3.5-2b-base-cpt")
model = AutoModelForCausalLM.from_pretrained("staeiou/bartleby-dlo-qwen3.5-2b-base-cpt")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use staeiou/bartleby-dlo-qwen3.5-2b-base-cpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/staeiou/bartleby-dlo-qwen3.5-2b-base-cpt

SGLang

How to use staeiou/bartleby-dlo-qwen3.5-2b-base-cpt 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 "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt" \
    --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": "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt",
		"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 "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt" \
        --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": "staeiou/bartleby-dlo-qwen3.5-2b-base-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use staeiou/bartleby-dlo-qwen3.5-2b-base-cpt with Docker Model Runner:
```
docker model run hf.co/staeiou/bartleby-dlo-qwen3.5-2b-base-cpt
```

bartleby-dlo-qwen3.5-2b-base-cpt

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---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.5-2B-Base/blob/main/LICENSE
base_model:
- Qwen/Qwen3.5-2B-Base
---

# BartlebyGPT Dead Letter Office (DLO-Base)

The BartlebyGPT Dead Letter Office (DLO-Bae) is a continued pretraining (CPT) of Qwen/Qwen3.5-2B-Base. CPT was run on ~62M tokens of Melvillian prose, over 1 epoch with TRL.

It is not trained for instruction following or conversation. It is intended to be fine-tuned.