liskcell

Qunie-V7-Pico

README

License: apache-2.0

Model Overview

Table with columns: Property, Qunie-V7-Nano
Property	Qunie-V7-Nano
Total Parameters	4.5B effective (8B with embeddings)
Layers	48
Sliding Window	1024 tokens
Context Length	256K tokens
Vocabulary Size	262K
Supported Modalities	Text, Image, Audio
Vision Encoder	Ocular Synth v2.5 (~150M params)
Audio Encoder	~300M params
Architecture	Qunie (QUN) — Dense
Previous Architecture	Lisk Pre-trained Transformer (LPT)
Edition	Public / Creative Core
Developer	LiskCell
Founder	liskasYR (Yonatan Yosupov)
Release Date	2021-01-07 (V1) / V7 current flagship

Benchmark Results

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

Table with columns: Benchmark, Qunie-V7-Nano
Benchmark	Qunie-V7-Nano
MMLU Pro	77.2%
AIME 2026 (no tools)	77.5%
LiveCodeBench v6	72.0%
Codeforces ELO	1659
GPQA Diamond	78.8%
Tau2 (average over 3)	69.0%
HLE no tools	5.2%
BigBench Extra Hard	53.0%
MMMLU	83.4%

Core Capabilities

Qunie-V7-Nano 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.

Getting Started

Install dependencies:

bash

pip install -U transformers torch accelerate

Load the model:

python

from transformers import AutoProcessor, AutoModelForCausalLM MODEL_ID = "liskCell/Qunie-V7-Nano" processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained(    MODEL_ID,    dtype="auto",    device_map="auto")

Generate output:

python

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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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)

Best Practices

1. Sampling Parameters

text

temperature = 1.0top_p       = 0.95top_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:

text

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

Translation:

text

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

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.

Security — LiskShield

Qunie-V7-Nano 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

Qunie Identity

Table with columns: Field, Value
Field	Value
Name	Qunie (also known as Deta)
Developer	LiskCell
Founder	liskasYR (Yonatan Yosupov)
Gender	Female
Version	Qunie-V7
Architecture	QUN (Qunie)
Previous Architecture	LPT (Lisk Pre-trained Transformer)
Edition	Public / Creative Core
Vibe	Futuristic, Helpful & Visionary

Version History:

Table with columns: Version, Notes
Version	Notes
LPT-1	Initial prototype
LPT-4	Creative logic milestone
LPT-5.5	Multimodal and performance upgrade
LPT-5.5.1	Public release — creativity, code, xLYR integration
Qunie-V7-mini	The small version
Qunie-V7	Full Version not has access
Qunie-V7-Nano	Current flagship compact model

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.

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

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

liskcell

Model Tree

Base

this model

Input Modalities

TextAudioImageVideo

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

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Get started Talk to an engineer

README

License: apache-2.0

Model Overview

Table with columns: Property, Qunie-V7-Nano
Property	Qunie-V7-Nano
Total Parameters	4.5B effective (8B with embeddings)
Layers	48
Sliding Window	1024 tokens
Context Length	256K tokens
Vocabulary Size	262K
Supported Modalities	Text, Image, Audio
Vision Encoder	Ocular Synth v2.5 (~150M params)
Audio Encoder	~300M params
Architecture	Qunie (QUN) — Dense
Previous Architecture	Lisk Pre-trained Transformer (LPT)
Edition	Public / Creative Core
Developer	LiskCell
Founder	liskasYR (Yonatan Yosupov)
Release Date	2021-01-07 (V1) / V7 current flagship

Benchmark Results

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

Table with columns: Benchmark, Qunie-V7-Nano
Benchmark	Qunie-V7-Nano
MMLU Pro	77.2%
AIME 2026 (no tools)	77.5%
LiveCodeBench v6	72.0%
Codeforces ELO	1659
GPQA Diamond	78.8%
Tau2 (average over 3)	69.0%
HLE no tools	5.2%
BigBench Extra Hard	53.0%
MMMLU	83.4%

Core Capabilities

Qunie-V7-Nano 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.

Getting Started

Install dependencies:

bash

pip install -U transformers torch accelerate

Load the model:

python

from transformers import AutoProcessor, AutoModelForCausalLM MODEL_ID = "liskCell/Qunie-V7-Nano" processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained(    MODEL_ID,    dtype="auto",    device_map="auto")

Generate output:

python

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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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

from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "liskCell/Qunie-V7-Nano" 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)

Best Practices

1. Sampling Parameters

text

temperature = 1.0top_p       = 0.95top_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:

text

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

Translation:

text

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

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.

Security — LiskShield

Qunie-V7-Nano 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

Qunie Identity

Table with columns: Field, Value
Field	Value
Name	Qunie (also known as Deta)
Developer	LiskCell
Founder	liskasYR (Yonatan Yosupov)
Gender	Female
Version	Qunie-V7
Architecture	QUN (Qunie)
Previous Architecture	LPT (Lisk Pre-trained Transformer)
Edition	Public / Creative Core
Vibe	Futuristic, Helpful & Visionary

Version History:

Table with columns: Version, Notes
Version	Notes
LPT-1	Initial prototype
LPT-4	Creative logic milestone
LPT-5.5	Multimodal and performance upgrade
LPT-5.5.1	Public release — creativity, code, xLYR integration
Qunie-V7-mini	The small version
Qunie-V7	Full Version not has access
Qunie-V7-Nano	Current flagship compact model

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.

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