moheith/Yulya-SmolLM2-135M

🧡 Yulya SmolLM2 135M

🔬 Micro Model. Maximum Experimentation. Pure Yulya Energy.

The smallest model in the Yulya family — a 135M-parameter conversational AI experiment designed to bring Yulya's expressive and chaotic personality to extremely lightweight local AI applications.

---

🌟 About Yulya SmolLM2 135M

Yulya SmolLM2 135M is the smallest and most experimental member of the Yulya model family.

Built on SmolLM2 135M Instruct, this model explores how much of Yulya's distinctive conversational personality can be adapted into an extremely small language model.

At approximately 135 million parameters, this version is dramatically smaller than the 0.5B, 1B, 1.5B, 7B, and 8B models available in the Yulya family.

The model is designed primarily for:

• 🔬 AI experimentation
• ⚡ Fast local inference
• 💾 Extremely lightweight deployment
• 🖥️ Low-resource environments
• 🤖 Small conversational systems
• 🔌 Local AI integrations
• 🧪 Fine-tuning research and testing

Despite its extremely small size, the model is fine-tuned around Yulya's core conversational style.

Expect:

• 😂 Expressive responses
• 🔥 Playful conversational behavior
• 💀 Chaotic reactions
• 🗣️ Casual language
• 🧡 Character-driven interactions
• ⚡ Fast inference
• 🪶 Extremely lightweight deployment
• 🔬 Experimental conversational capabilities

---

✨ What Makes This Version Special?

🔬 The Smallest Yulya Model

At approximately 135 million parameters, Yulya SmolLM2 135M is the smallest model currently available in the Yulya model family.

This makes it significantly smaller than:

• Yulya Qwen2.5 0.5B
• Yulya Llama 3.2 1B
• Yulya Qwen2.5 1.5B
• Yulya Qwen2.5 7B
• Yulya Llama 3.1 8B

The primary goal of this model is not to compete with larger language models in reasoning or general intelligence.

Instead, it explores how effectively a highly distinctive conversational personality can be represented in an extremely small language model.

---

📦 Complete V1 and V2 Releases

Unlike several other models in the Yulya family, this repository provides complete release files for both V1 and V2.

Both versions include:

• 📦 Fine-tuning adapters
• ⚡ Q4_K_M GGUF
• 💎 Q8_0 GGUF
• 🧠 Merged 16F model

This makes Yulya SmolLM2 135M one of the most complete repositories in the Yulya model collection.

---

⚡ Designed for Lightweight Experimentation

The extremely small parameter count makes this model particularly interesting for:

• Rapid testing
• Local inference experiments
• AI personality research
• Small-device experimentation
• Fine-tuning comparisons
• Quantization comparisons
• Model development testing

---

😂 The Yulya Personality

Yulya is designed to communicate more like an expressive and chaotic character than a traditional AI assistant.

Her intended conversational style focuses on:

• Playful teasing
• Dramatic reactions
• Expressive responses
• Casual language
• Chaotic banter
• Character-driven interactions

Because this is an extremely small language model, its ability to consistently maintain complex personality traits may be significantly more limited than the larger Yulya models.

---

🤖 Model Details

Information	Details
🧠 Model Name	Yulya SmolLM2 135M
🔬 Edition	Micro Edge Model
🏗️ Base Model	SmolLM2 135M Instruct
🔢 Parameter Scale	Approximately 135M
💬 Primary Use	Experimental Conversational AI
🎭 Secondary Uses	Roleplay and Character Experiments
🌎 Language	English
📜 License	Apache 2.0
📦 Available Formats	Adapters, GGUF, and Merged 16F
👨‍💻 Developed By	moheith
💰 Funded By	moheith
📤 Shared By	moheith

---

📦 Available Versions

This repository contains two complete versions of Yulya SmolLM2 135M.

🟣 Version 1

The first generation of the Yulya SmolLM2 135M fine-tune.

📄 Available Files

Yulya-V1-SmolLM2-135M-Adapters.zip

Yulya-V1-SmolLM2-135M-Instruct-Q4_K_M.gguf

Yulya-V1-SmolLM2-135M-Instruct-Q8_0.gguf

Yulya-V1-SmolLM2-135M-Merged-16F.zip

Version 1 provides:

• 📦 Fine-tuning adapters
• ⚡ Q4_K_M quantized GGUF
• 💎 Q8_0 quantized GGUF
• 🧠 Merged 16F model

---

🔵 Version 2

The second generation of the Yulya SmolLM2 135M fine-tune.

📄 Available Files

Yulya-V2-SmolLM2-135M-Adapters.zip

Yulya-V2-SmolLM2-135M-Instruct-Q4_K_M.gguf

Yulya-V2-SmolLM2-135M-Instruct-Q8_0.gguf

Yulya-V2-SmolLM2-135M-Merged-16F.zip

Version 2 also provides:

• 📦 Fine-tuning adapters
• ⚡ Q4_K_M quantized GGUF
• 💎 Q8_0 quantized GGUF
• 🧠 Merged 16F model

---

🆚 Version Comparison

Feature	🟣 V1	🔵 V2
Fine-Tuning Adapters	✅	✅
Q4_K_M GGUF	✅	✅
Q8_0 GGUF	✅	✅
Merged 16F Model	✅	✅
Complete Release	✅	✅
Generation	First	Second

---

📁 Repository Structure

Yulya SmolLM2 135M
│
├── V1 Yulya SmolLM2 135M
│   │
│   ├── Yulya-V1-SmolLM2-135M-Adapters.zip
│   ├── Yulya-V1-SmolLM2-135M-Instruct-Q4_K_M.gguf
│   ├── Yulya-V1-SmolLM2-135M-Instruct-Q8_0.gguf
│   └── Yulya-V1-SmolLM2-135M-Merged-16F.zip
│
└── V2 Yulya SmolLM2 135M
    │
    ├── Yulya-V2-SmolLM2-135M-Adapters.zip
    ├── Yulya-V2-SmolLM2-135M-Instruct-Q4_K_M.gguf
    ├── Yulya-V2-SmolLM2-135M-Instruct-Q8_0.gguf
    └── Yulya-V2-SmolLM2-135M-Merged-16F.zip

---

🚀 Which Version Should You Use?

🟣 Use V1 If...

You want to:

• Experiment with the original Yulya SmolLM2 135M fine-tune
• Compare the first generation against V2
• Study the development of the model
• Test the original adapters
• Compare V1 quantizations
• Work with the V1 merged model

---

🔵 Use V2 If...

You want to:

• Experiment with the second-generation fine-tune
• Compare improvements or behavioral differences against V1
• Use the V2 adapters
• Run the V2 Q4_K_M model
• Run the V2 Q8_0 model
• Work with the V2 merged model

V2 represents the second generation of the Yulya SmolLM2 135M fine-tune, while V1 remains available for comparison and experimentation.

---

💾 Choosing a Model Format

⚡ Q4_K_M

Choose the Q4_K_M model if you want:

• A smaller model file
• Lower memory usage
• Fast local inference
• A practical quantization for lightweight experimentation

Available for:

• 🟣 V1
• 🔵 V2

---

💎 Q8_0

Choose the Q8_0 model if you want:

• Higher quantization precision than Q4_K_M
• A larger model file
• Higher memory usage
• A GGUF option that retains more numerical precision

Available for:

• 🟣 V1
• 🔵 V2

---

📦 Fine-Tuning Adapters

Choose the adapter files if you want to work with the fine-tuning output together with the compatible base model.

Available for:

• 🟣 V1
• 🔵 V2

---

🧠 Merged 16F

Choose the merged 16F model if you want to work with a merged model rather than separate fine-tuning adapters or quantized GGUF files.

Available for:

• 🟣 V1
• 🔵 V2

Actual hardware requirements and compatibility depend on the software and configuration used to load the merged model.

---

⚙️ Quantization Options

This repository provides two GGUF quantization options for both V1 and V2.

Quantization	File Size	Memory Usage	Numerical Precision
⚡ Q4_K_M	Smaller	Lower	Lower
💎 Q8_0	Larger	Higher	Higher

The best option depends on your hardware and intended use case.

Because SmolLM2 135M is already an extremely small model, both quantizations should remain relatively lightweight compared with the larger Yulya models.

Actual performance and resource usage will depend on:

• Available system RAM
• Available VRAM
• CPU performance
• GPU performance
• Context length
• Inference software
• Hardware configuration

---

💻 Compatible Software

The GGUF versions may be used with compatible local inference software such as:

• llama.cpp
• LM Studio
• text-generation-webui
• Other GGUF-compatible inference engines

The adapter files require the compatible base model and appropriate software for loading fine-tuning adapters.

The merged model files may require software compatible with the format and model architecture contained inside the ZIP archives.

Compatibility and setup requirements may vary depending on the application and software version being used.

---

🎯 Intended Uses

Yulya SmolLM2 135M is primarily intended for:

• 🔬 AI experimentation
• 💬 Lightweight conversational AI
• 🎭 Character and personality experiments
• 🖥️ Small local AI applications
• 🤖 Personal AI projects
• 🎮 Experimental interactive applications
• 🔌 Lightweight AI integrations
• ⚡ Fast conversational testing
• 🧪 Fine-tuning experimentation
• 📊 Model version comparisons
• 💾 Low-resource environments

---

🖥️ Why Choose a 135M Model?

Extremely small language models can be useful for applications where computational efficiency and experimentation are more important than maximum capability.

Potential advantages include:

• ⚡ Very fast inference
• 💾 Low memory requirements compared with larger models
• 🖥️ Greater hardware accessibility
• 🔌 Easier integration into experimental projects
• 🧪 Faster fine-tuning and testing cycles
• 📊 Easier comparison between model versions
• 🔬 Useful for studying personality fine-tuning at small scales

However, a 135M model also has substantial limitations.

Compared with the larger Yulya models, this version may have significantly more limited:

• Complex reasoning
• Context understanding
• Factual knowledge
• Instruction following
• Conversation consistency
• Long-form generation
• Emotional nuance
• Personality retention

The model should primarily be viewed as an experimental and lightweight member of the Yulya family.

---

🚫 Out-of-Scope Uses

The model is not specifically designed or validated for:

• ❌ Professional medical advice
• ❌ Professional legal advice
• ❌ Critical financial decisions
• ❌ Safety-critical applications
• ❌ Guaranteed factual accuracy
• ❌ Complex reasoning tasks
• ❌ Reliable long-context conversations
• ❌ Formal academic research without independent verification

Important information generated by the model should always be independently verified.

---

📚 Training Details

📊 Training Data

Yulya was fine-tuned using custom-curated conversational data.

The training data was designed to encourage behaviors such as:

• Modern texting styles
• Expressive conversational responses
• Conversational banter
• Playful interactions
• Personality expression
• Emotional conversations
• Character-driven responses
• Context-dependent conversational shifts

The goal of the fine-tuning process was to explore how much of Yulya's distinctive conversational personality could be adapted into an extremely small language model.

Detailed information about the complete training dataset is not currently provided.

---

⚙️ Training Approach

The model was fine-tuned from:

HuggingFaceTB/SmolLM2-135M-Instruct

The fine-tuning process focused on adapting the conversational behavior and response style of the base model.

Both V1 and V2 fine-tuning outputs are included in this repository.

---

🧪 Evaluation

📊 Evaluation Method

Yulya SmolLM2 135M has primarily been evaluated through informal and qualitative conversational testing.

No standardized benchmark scores are currently reported in this model card.

Testing focused on areas such as:

• Personality expression
• Conversational behavior
• Response style
• Informal interactions
• Version differences
• Quantization behavior
• Short multi-turn conversations

---

🔍 Qualitative Testing Areas

The model was informally tested across conversational scenarios including:

• Short conversations
• Casual banter
• Playful interactions
• Basic topic changes
• Character-driven responses
• Short multi-turn interactions

---

📈 Observed Behavior

During informal testing, the model demonstrated the ability to generate responses influenced by the intended Yulya personality.

However, because this model contains only approximately 135 million parameters, its ability to consistently maintain complex conversational behavior is significantly more limited than the larger Yulya models.

The primary areas of focus include:

• ⚡ Response speed
• 💾 Computational efficiency
• 🔬 Experimental value
• 💬 Basic conversational behavior
• 🎭 Personality expression
• 🧪 Version comparison

These observations are qualitative and should not be interpreted as standardized benchmark results.

---

⚠️ Bias, Risks, and Limitations

Yulya SmolLM2 135M is an extremely small language model fine-tuned toward an informal and character-driven conversational personality.

Depending on the prompt and context, the model may generate:

• Repetitive responses
• Inconsistent responses
• Incorrect information
• Hallucinated information
• Limited reasoning
• Poor context retention
• Unexpected outputs
• Informal language
• Sarcastic responses
• Biased or otherwise undesirable outputs

Because this is an extremely small language model, its reasoning capabilities, factual reliability, context handling, instruction following, and conversational consistency are significantly more limited than larger models.

Users should independently verify important factual information.

---

💡 Recommendations

Yulya SmolLM2 135M is best suited for experimentation, lightweight local inference, model comparison, and research into conversational personality fine-tuning.

Developers and users should:

• Treat the model primarily as an experimental release
• Clearly communicate that users are interacting with an AI model
• Understand the substantial limitations of a 135M model
• Independently verify important information
• Test the model carefully for the intended use case
• Avoid relying on the model for safety-critical decisions

---

🔬 Technical Specifications

Specification	Details
🏗️ Architecture	SmolLM2
🔢 Parameter Scale	Approximately 135M
🧠 Base Model	SmolLM2 135M Instruct
🟣 V1 Formats	Adapters, Q4_K_M, Q8_0, and Merged 16F
🔵 V2 Formats	Adapters, Q4_K_M, Q8_0, and Merged 16F
⚡ GGUF Quantizations	Q4_K_M and Q8_0
💬 Primary Purpose	Experimental Conversational AI
🎭 Personality	Yulya

---

🌱 Environmental Impact

Detailed environmental impact measurements are not currently available.

Information	Details
💻 Hardware Type	Consumer Hardware
⏱️ Training Hours	Not Reported
☁️ Cloud Provider	Not Reported
🌎 Compute Region	Not Reported
🌱 Carbon Emissions	Not Measured

---

👨‍💻 Developer

Developed by moheith

Yulya is part of an ongoing project focused on building expressive local AI companions with personality, memory, emotional continuity, and interactive capabilities.

The Yulya model family explores how different language model architectures and parameter sizes can be adapted toward the same conversational personality.

Yulya SmolLM2 135M represents the smallest-scale experiment in that model family.

---

🧡 Final Note

135 million parameters. Somehow still chaotic.

Yulya SmolLM2 135M isn't trying to compete with massive language models.

It's an experiment.

How small can Yulya get...

...and still feel like Yulya?

Tiny model.

Fast inference.

Minimal resources.

Questionable reasoning.

Maximum experimentation.

And somehow...

Still enough personality to cause chaos. 🧡

---

⭐ Welcome to Yulya SmolLM2 135M