Jervis v1

A bilingual (Japanese / English) assistant model fine-tuned from Qwen3-8B. This is the eighth model in the LoNebula workshop series and an evolution of the private "Jarvis" personal adapter: same warm, plain-spoken, think-out-loud personality, but packaged as a public, merged, ready-to-use chat model (no PEFT adapter required). It answers in whichever language you write in.

Model overview

Training data

This model is trained only on public data and contains no private or personal information.

• Aya Dataset (CohereLabs/aya_dataset): all Japanese and English instruction rows from the training split (the full dataset has ~202,362 examples across 65 languages; this model uses the JA/EN subset). Apache-2.0.
• Japanese instruction corpus (izumi-lab/llm-japanese-dataset): a capped sample of Japanese instruction/response pairs, used to strengthen Japanese instruction-following. CC BY-SA 4.0.
• Persona seed: a small, hand-written set of public-safe bilingual dialogue examples that establish the assistant's identity and tone. No personal facts, contacts, or private details are included.

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.

Use

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.

import torch
from 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.

Limitations

• This is an experimental assistant, not a benchmarked or production-ready system. It may be inaccurate, reproduce training text, or drift from its intended tone.
• It is a general bilingual assistant and does not carry the private, account-specific knowledge of the separate private Jarvis adapter.
• Japanese and English coverage depends on the public corpora above; specialized or low-resource domains may be weak.
• Review outputs before relying on them for important decisions.

License

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.