Instructions to use potteryrage/NekGPT-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use potteryrage/NekGPT-XL with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("potteryrage/NekGPT-XL")
model = AutoModelForCausalLM.from_pretrained("potteryrage/NekGPT-XL")
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 potteryrage/NekGPT-XL with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/potteryrage/NekGPT-XL

SGLang

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

Docker Model Runner
How to use potteryrage/NekGPT-XL with Docker Model Runner:
```
docker model run hf.co/potteryrage/NekGPT-XL
```

NekGPT XL

NekGPT XL is a full fine-tune of Qwen/Qwen3.5-0.8B-Base on authored twitter posts from @nekstoer.

This release is the larger follow-up to the original nekGPT toy model. The target was simple: make it more cohesive, less funny, and less likely to collapse immediately in short conversations.

Validation loss was best after epoch 1 and worsened slightly at epoch 2:

epoch 1: 0.4954
epoch 2: 0.5053

Description

The goal was not alignment polish or generic assistant quality. The goal was a model that still understands basic questions, but answers with the voice pressure of the source corpus instead of defaulting back into a helpful assistant persona.

The result is more coherent than the original GPT-2 release and less sanitized than the base Qwen checkpoint, but it still drifts under sustained multi-turn chat. It works best as an experimental voice model and chat toy, not as a reliable factual assistant.

Methodology

Corpus

Source account: @nekstoer
Collection method: GetXAPI scrape of authored posts only
Reposts: excluded
Replies: included
Quotes: included
Raw authored posts collected: 1526

Dataset shaping

The scrape was converted into higher-signal supervised examples:

standalone posts: write a tweet -> post
replies: reply to this post from @user: ... -> reply
quotes: quote tweet this post: ... -> quote tweet

To make the voice distribution more pronounced, a subset of standout standalone posts was repeated in the training split.

Effective dataset sizes for this run:

train: 1786
valid: 20
test: 20

Training

base model: Qwen/Qwen3.5-0.8B-Base
hardware: Apple Silicon MPS
max length: 384
epochs: 2
learning rate: 2e-5
batch size: 1
gradient accumulation: 8
gradient checkpointing: enabled

Known behavior

Strengths:

answers simple questions more coherently than the original nekGPT
holds the local tone and phrasing much more aggressively than base Qwen
stays chaotic without immediately collapsing into nonsense

Weaknesses:

still drifts in longer multi-turn chats
factual grounding is weak and can fail even on basic prompts
overfits recurring motifs, formatting quirks, and usernames from the corpus
the epoch-1 checkpoint had the best validation loss, while the final exported model is the epoch-2 artifact used for this release

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "potteryrage/NekGPT-XL"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)

prompt = "System: Output only the completion.\nUser: write a tweet\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=64,
    do_sample=True,
    temperature=1.05,
    top_p=0.98,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

License

Apache-2.0

Downloads last month: 3

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for potteryrage/NekGPT-XL

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

(66)

this model