Instructions to use marcuscedricridia/bananafish-522 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcuscedricridia/bananafish-522 with Transformers:

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

pipe = pipeline("text-generation", model="marcuscedricridia/bananafish-522")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marcuscedricridia/bananafish-522")
model = AutoModelForCausalLM.from_pretrained("marcuscedricridia/bananafish-522")
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 marcuscedricridia/bananafish-522 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcuscedricridia/bananafish-522"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcuscedricridia/bananafish-522",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marcuscedricridia/bananafish-522

SGLang

How to use marcuscedricridia/bananafish-522 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 "marcuscedricridia/bananafish-522" \
    --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": "marcuscedricridia/bananafish-522",
		"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 "marcuscedricridia/bananafish-522" \
        --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": "marcuscedricridia/bananafish-522",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use marcuscedricridia/bananafish-522 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for marcuscedricridia/bananafish-522 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for marcuscedricridia/bananafish-522 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marcuscedricridia/bananafish-522 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="marcuscedricridia/bananafish-522",
    max_seq_length=2048,
)

Docker Model Runner
How to use marcuscedricridia/bananafish-522 with Docker Model Runner:
```
docker model run hf.co/marcuscedricridia/bananafish-522
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

bananafish-522

Release: May 2025
Base Model: Qwen3
Type: Instruction-tuned GLT (General Language Transformer)

Overview

bananafish-522 is an update over bananafish-0517, trained for 5,000 steps on the 49,000-row ITP dataset. This is roughly one-fifth of a full epoch, so only a partial pass over the dataset. To improve coverage, the dataset was shuffled — ensuring samples from later parts were still seen during training.

What's Improved

Stronger coherence
Fewer output artifacts
More consistent instruction-following
Better formatting across responses
More usable as a base for creative or chat fine-tuning

Remaining Issues

Post-response artifacts
Sometimes, the model appends:
- One word in a foreign language
- Or repeats a single Chinese character until max tokens
Much rarer than in bananafish-0517. Likely caused by:
- Incomplete training (only 5,000 steps)
- Or possibly improper EOS handling (generating past <|im_end|>)
To address this, <|im_end|> was explicitly added as the EOS token in the generation config.
High hallucination rate
Especially for anything dated 2024 or later. Always verify facts before use.

Purpose

bananafish-522 was built as a clean instruction-following base model without Qwen3’s "thinking toggle." It aims to support creative writing and roleplay fine-tuning where chain-of-thought generation is unwanted or intrusive.

Training Details

Dataset: ITP (Instruction Tuning Public)
Size: 49,000 examples
Steps: 5,000
Coverage: ~1/5 of a full epoch
Shuffled: Yes — to include later samples early in training
Contents: Instructions, chat, Q&A, STEM, writing, etc.

Compared to the older 10k-row NoRobots dataset, ITP is larger, more diverse, and showed better alignment — even with fewer total steps. The difference came from dataset quality, not scale.

Usage

Use in any transformer-compatible chat interface. Make sure to stop generation at <|im_end|>.

Chat Template

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What's the capital of France?<|im_end|>
<|im_start|>assistant

Generation should stop at the next <|im_end|> token.

Notes

This is still a proof of concept
Only partially trained — fine-tuning for specific tasks is recommended
A full version trained on all 49k rows with style tuning is planned

Downloads last month: 4

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for marcuscedricridia/bananafish-522

Quantizations

1 model