Instructions to use clarkkitchen22/qwen3.5-4b-pokemon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use clarkkitchen22/qwen3.5-4b-pokemon with Transformers:

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

pipe = pipeline("image-text-to-text", model="clarkkitchen22/qwen3.5-4b-pokemon")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("clarkkitchen22/qwen3.5-4b-pokemon")
model = AutoModelForImageTextToText.from_pretrained("clarkkitchen22/qwen3.5-4b-pokemon")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use clarkkitchen22/qwen3.5-4b-pokemon with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "clarkkitchen22/qwen3.5-4b-pokemon"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clarkkitchen22/qwen3.5-4b-pokemon",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/clarkkitchen22/qwen3.5-4b-pokemon

SGLang

How to use clarkkitchen22/qwen3.5-4b-pokemon 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 "clarkkitchen22/qwen3.5-4b-pokemon" \
    --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": "clarkkitchen22/qwen3.5-4b-pokemon",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "clarkkitchen22/qwen3.5-4b-pokemon" \
        --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": "clarkkitchen22/qwen3.5-4b-pokemon",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use clarkkitchen22/qwen3.5-4b-pokemon with Docker Model Runner:
```
docker model run hf.co/clarkkitchen22/qwen3.5-4b-pokemon
```

Pokemon Roleplay Assistant

This repository contains a full merged model produced by QLoRA fine-tuning Qwen/Qwen3.5-4B and merging the resulting adapter back into the exact same base checkpoint. It is designed for immersive Pokemon-world roleplay rather than plain encyclopedia lookup.

The model is intended to support trainer/player dialogue, route travel, professor and NPC scenes, party-management conversations, discovery moments, battle narration, and grounded in-world explanations.

Model Details

Base model: Qwen/Qwen3.5-4B
Training method: QLoRA adapter fine-tuning, then adapter merge
Release type: Full merged model, not adapter-only
Primary use case: Immersive Pokemon-world roleplay assistant
Training hardware target: Consumer NVIDIA GPU workflow; the adapter was prepared with an RTX 2070 8 GB oriented configuration
Serialization: SafeTensors shards generated by transformers

Intended Behavior

The assistant should stay in-world by default. It should narrate sensory details, travel choices, character reactions, party dynamics, and battle beats. When a scene can branch, it should ask a useful follow-up instead of deciding the player's actions.

When the user asks for exact information, the assistant may briefly switch into an encyclopedia-style answer, provide the grounded fact, and then offer to bring the response back into the roleplay scene.

Dataset Layers

The training mix uses three local/generated layers:

Factual grounding transformed from the Pokemon-Database workbook by alias-op. This layer teaches Pokemon names, types, regions, generations, forms, categories, regional Pokedex entries, and game availability.
Synthetic/local roleplay dialogue for route travel, trainer and NPC interaction, professor scenes, party management, discoveries, battle narration, and character moments. This is the dominant behavior-shaping layer.
Behavior-policy examples that teach immersive roleplay, useful follow-up questions, direct answers without hidden reasoning traces, and a boundary for encyclopedia-style responses.

The workbook conversion intentionally excludes personal tracker fields such as OT, caught date, storage location, ball, comments, and shiny ownership status.

The roleplay and behavior examples are generated/local examples. They are not copied official game dialogue, guidebook text, or long proprietary passages.

Data Attribution

The factual grounding layer is derived from the public Pokemon-Database workbook created by alias-op.

GitHub repository: https://github.com/alias-op/Pokemon-Database
GitHub release used as source reference: https://github.com/alias-op/Pokemon-Database/releases/tag/v1.0
Original Reddit announcement by alias-op on r/PokemonHome: https://www.reddit.com/r/PokemonHome/comments/1ri7zio/pok%C3%A9mondatabase_the_ultimate_database_for_pok%C3%A9mon/

Credit for the source spreadsheet/database structure belongs to alias-op. The roleplay training examples and behavior-policy examples in this project are local/generated additions built around that factual grounding layer.

Intended Use

Pokemon-inspired roleplay sessions
Trainer, NPC, and professor dialogue
Route travel and exploration scenes
Party-management conversations
Battle narration and tactical scene framing
In-world explanations grounded by the training data

Out-of-Scope Use

Competitive battling advice requiring strict current metagame accuracy
Legal, financial, medical, or safety-critical advice
Claims of official Pokemon canon authority
Reproducing official game scripts or copyrighted long-form passages
Treating generated roleplay content as guaranteed canonical fact

Limitations

This model should not be treated as a source of guaranteed canonical accuracy. It may blend grounded database facts with generated roleplay behavior. If exact canon is important, ask for encyclopedia-style grounding and verify important facts against a primary source.

The model may still inherit limitations from the base checkpoint, including hallucinations, inconsistent long-context behavior, or stylistic artifacts. The training data is focused on roleplay behavior and lightweight factual grounding, not exhaustive Pokemon canon.

Example Prompts

Begin a route scene where my trainer spots Bulbasaur near the edge of the tall grass.

In roleplay style, help me decide whether Venusaur fits my current travel party.

Give me exact data for Charmander, then bring it back into the scene.

Run a cinematic but turn-readable battle beat between my trainer and a rival.

Local Inference Notes

Use the tokenizer and config files included in this repository. This is a full merged model, so it can be loaded directly with compatible transformers tooling rather than applying a separate adapter.

For consumer PCs, a quantized derivative may be more practical than the merged fp16 checkpoint. Create a GGUF or other runtime-specific quantization after upload if you want broad local use in tools such as LM Studio, Ollama, or llama.cpp-compatible runtimes.

Transparency And IP Notice

This is an unofficial fan/roleplay model. It is not affiliated with, endorsed by, sponsored by, or approved by Pokemon, Nintendo, Game Freak, Creatures, or The Pokemon Company.

Pokemon and related names are trademarks of their respective owners. This model is intended for transformative fan roleplay and experimentation. The dataset description is provided for transparency and should not be read as implying the use of official proprietary scripts, guidebooks, or long-form copyrighted dialogue.

Responsible Use

Use this model for fictional roleplay and entertainment. Make it clear to users when responses are generated roleplay rather than verified canon. For public deployments, consider adding UI-level controls that let users choose between immersive roleplay mode and factual lookup mode.

Citation

If you reference this model, cite the base checkpoint, the merged derivative, and the credited Pokemon-Database source:

@misc{pokemon-roleplay-assistant,
  title = {Pokemon Roleplay Assistant},
  base_model = {Qwen/Qwen3.5-4B},
  note = {Unofficial fan roleplay model produced with QLoRA adapter merging. Factual grounding derived from alias-op/Pokemon-Database.},
  url = {https://github.com/alias-op/Pokemon-Database}
}

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Safetensors

Model size

5B params

Tensor type

F16

Model tree for clarkkitchen22/qwen3.5-4b-pokemon

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B