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LoNebula
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Model Details
Model Provider
LoNebula
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
| Property | Details |
|---|---|
| Base model | Qwen3-8B (8.2B parameters, Apache-2.0) |
| Method | 4-bit QLoRA (NF4 + double quant, bf16 compute), then merged into the base |
| Languages | Japanese, English |
| License | Apache-2.0 (adapter merged; subject to the base model license) |
| Author | LoNebula (寝坊ロケット / Nebou Rocket) |
| Weights format | Merged bfloat16, loads directly with transformers |
This model is trained only on public data and contains no private or personal information.
The private, identity-specific corpus that personalizes the account owner lives only in the separate private "Jarvis Personal" repository and is never part of this public model.
The repository contains merged weights, a tokenizer, and a generation_config.json, so it loads directly and generates with sensible defaults. Use a GPU with enough memory for the quantized or half-precision base.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "LoNebula/jervis-v1"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "system", "content": "You are Jervis, a bilingual (Japanese/English) assistant. Answer in the user's language."}, {"role": "user", "content": "難しいバグにぶつかったとき、どう進めればいい？"},]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", chat_template_kwargs={"enable_thinking": False}, # Qwen3: non-thinking chat mode).to(model.device)output = model.generate(inputs, max_new_tokens=512)print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))Install the runtime with pip install torch transformers accelerate. The generation_config.json sets temperature=0.7, top_p=0.8, top_k=20; override as needed. This model was fine-tuned with the Qwen3 chat template in non-thinking mode (enable_thinking=False), so pass the same chat_template_kwargs at inference; if your transformers version does not recognize the kwarg, drop it and the template falls back to its default.
Apache-2.0, subject to the base model license (Qwen3-8B, Apache-2.0) and the licenses of the public training data (Aya: Apache-2.0; izumi-lab Japanese corpus: CC BY-SA 4.0). Review source terms before redistribution.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "LoNebula/jervis-v1"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "system", "content": "You are Jervis, a bilingual (Japanese/English) assistant. Answer in the user's language."}, {"role": "user", "content": "難しいバグにぶつかったとき、どう進めればいい？"},]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", chat_template_kwargs={"enable_thinking": False}, # Qwen3: non-thinking chat mode).to(model.device)output = model.generate(inputs, max_new_tokens=512)print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))