Model Details

• Model name: Mosslight 4B
• Model ID: ttrpg/mosslight-4b
• Base model: Qwen/Qwen3.5-4B
• Derivative type: fine-tuned and merged full-weight release
• Architecture: Qwen3_5ForConditionalGeneration
• Model type: vision-language causal generation
• Parameters: approximately 4B
• Native context length: 262,144 tokens, as inherited from the base config
• License: Apache 2.0, inherited from the base model

Lineage

This model is a fine-tuned, merged derivative of Qwen3.5-4B from Alibaba Cloud/Qwen. The original Apache 2.0 license is preserved in LICENSE, and derivative attribution is documented in NOTICE.

Training and merge details should be completed before publishing a final public version.

Training Details

• Base checkpoint: Qwen/Qwen3.5-4B
• Fine-tuning method: TODO
• Training data: TODO
• Merge method: TODO
• Output format: merged full weights in sharded Safetensors format
• Post-training evaluation: TODO

Files

• config.json: model architecture and multimodal configuration.
• model.safetensors-00001-of-00002.safetensors
• model.safetensors-00002-of-00002.safetensors
• model.safetensors.index.json
• tokenizer.json, tokenizer_config.json, vocab.json, merges.txt
• chat_template.jinja
• preprocessor_config.json, video_preprocessor_config.json
• LICENSE, NOTICE

Usage

Install a Transformers build that supports Qwen3.5, then load the model using the standard Hugging Face APIs.

python
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from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "ttrpg/mosslight-4b"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Briefly introduce yourself."},
        ],
    }
]

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=256)
print(processor.decode(outputs[0], skip_special_tokens=True))

Serving

Use serving frameworks only after confirming they support Qwen3.5 model classes and the required multimodal processor files.

Example model identifier:

bash
Copy code
ttrpg/mosslight-4b

Intended Use

Mosslight 4B is intended for experimentation with compact multimodal assistant workflows, text generation, visual question answering, and local model serving.

Limitations

• No independent benchmark results are published for this custom release yet.
• Behavior and safety characteristics should be evaluated for your target use case before deployment.
• This model inherits limitations from the Qwen3.5-4B base model and from the fine-tuning and merge process used for this release.

Attribution

Mosslight 4B is a fine-tuned, merged derivative based on Qwen3.5-4B. Please retain the Apache 2.0 license and attribution notices when redistributing this model or derivatives of it.