Model Overview

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Benchmark Results

Evaluation results are for the instruction-tuned variant of Qunie-V7-mini.

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Core Capabilities

Qunie-V7-mini handles a broad range of tasks across text, vision, and audio:

• Thinking — Built-in reasoning mode that lets the model think step-by-step before answering.
• Long Context — 128K token context window.
• Image Understanding — Object detection, document/PDF parsing, screen and UI understanding, chart comprehension, OCR (multilingual), handwriting recognition, and pointing.
• Video Understanding — Analyze video by processing sequences of frames.
• Interleaved Multimodal Input — Freely mix text and images in any order within a single prompt.
• Function Calling — Native support for structured tool use, enabling agentic workflows.
• Coding — Code generation, completion, and correction.
• Multilingual — Optimized for Hebrew and English. Pre-trained on 140+ languages.
• Audio — Automatic speech recognition (ASR) and speech-to-translated-text translation.
• Creative Workflows — liskFlow integration for brainstorming, branding, music concepts, and futuristic design.
• Human-Feeling Personality — Warm, emotionally aware, conversational behavior built into the model core.

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Getting Started

Install dependencies:

bash
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pip install -U transformers torch accelerate

Load the model:

python
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from transformers import AutoProcessor, AutoModelForCausalLM

MODEL_ID = "liskCell/Qunie-V7-mini"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

Generate output:

python
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messages = [
    {"role": "system", "content": "You are Qunie, developed by LiskCell."},
    {"role": "user", "content": "Hey, introduce yourself!"},
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
processor.parse_response(response)

python
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from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "liskCell/Qunie-V7-mini"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "https://your-audio-url.wav"},
            {"type": "text", "text": "Transcribe the following speech segment."},
        ]
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
processor.parse_response(response)

python
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from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "liskCell/Qunie-V7-mini"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://your-image-url.png"},
            {"type": "text", "text": "What is shown in this image?"}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
processor.parse_response(response)

python
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from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "liskCell/Qunie-V7-mini"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "https://your-video-url.mp4"},
            {"type": "text", "text": "Describe this video."}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
processor.parse_response(response)

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Best Practices

1. Sampling Parameters

markdown
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temperature = 1.0
top_p       = 0.95
top_k       = 64

2. Thinking Mode

• Enable thinking: Include <|think|> token at the start of the system prompt.
• Disable thinking: Remove the token.
• When enabled, output structure: <|channel>thought\n [Internal reasoning] <channel|> [Final answer]

3. Multi-Turn Conversations

Do not include thinking content from previous turns in conversation history. Only the final response is passed forward.

4. Modality Order

Place image and/or audio content before the text in your prompt for optimal performance.

5. Variable Image Resolution

Supported token budgets: 70 / 140 / 280 / 560 / 1120

• Lower budgets → faster inference (captioning, video)
• Higher budgets → more detail (OCR, document parsing)

6. Audio Prompt Templates

ASR:

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Transcribe the following speech segment in {LANGUAGE}.
Only output the transcription. Write numbers as digits.

Translation:

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Transcribe the speech in {SOURCE_LANGUAGE}, then translate to {TARGET_LANGUAGE}.
Output: transcription, newline, "{TARGET_LANGUAGE}: ", translation.

7. Length Limits

• Audio: max 30 seconds
• Video: max 60 seconds at 1 frame/second

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Model Data

Training Dataset

Pre-training dataset includes web documents, code, images, and audio across 140+ languages, with a knowledge cutoff of 2025-12-21. Key components:

• Web Documents — Broad range of linguistic styles, topics, and vocabulary in 140+ languages.
• Code — Syntax and patterns of programming languages for code generation and understanding.
• Mathematics — Logical reasoning and symbolic representation.
• Images — Wide range of images for visual analysis and data extraction.

Data Preprocessing

• CSAM Filtering — Applied at multiple stages to exclude harmful and illegal content.
• Sensitive Data Filtering — Personal information and sensitive data removed from training sets.
• Content Quality Filtering — Based on LiskCell content quality and safety standards.

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Security — LiskShield

Qunie-V7-mini ships with LiskShield, LiskCell's built-in safety protocol:

• Encryption: AES-256-GCM / Quantum-lite Encryption
• Data Privacy: User data is localized and protected
• Content Filtering: Context-aware filtering active at inference time
• Jailbreak Resistance: Model refuses instruction-override attempts via chat
• Hacking Protection: Refuses unauthorized access requests with her emotional protective phrase

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Qunie Identity

Version History:

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Usage and Limitations

Intended Usage

• Content Creation — Text generation, chatbots, summarization, image data extraction, audio processing.
• Research and Education — NLP research, language learning, knowledge exploration.
• Creative Workflows — Branding, music concepts, futuristic design via liskFlow.
• Development — Code generation, agentic workflows, function calling.

Limitations

• Model performance depends on training data quality and diversity.
• May struggle with highly open-ended or ambiguous tasks.
• Does not have real-time internet access (knowledge cutoff: 2025-12-21).
• May generate incorrect factual statements — not a knowledge base.
• Natural language nuances, sarcasm, and figurative language may be misinterpreted.

Ethical Considerations

• Bias and Fairness — Training data was filtered and evaluated to mitigate socio-cultural biases.
• Misinformation — Developers are encouraged to implement appropriate content safety layers.
• Privacy — Training data was filtered for personal information removal.
• Transparency — This model card summarizes architecture, capabilities, limitations, and evaluation.

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Qunie-V7-mini — built by LiskCell. Human first, AI second.