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Model: kedarcv/Clair-3B
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation
+- conversational
+- assistant
+- fine-tuned
+- gguf
+- ollama
+- cpu-inference
+datasets:
+- custom
+metrics:
+- accuracy
+---
+# Clair-3B
+
+Clair-3B is a highly capable 3-billion parameter language model designed for advanced conversational AI, coding assistance, and complex reasoning tasks.
+
+## Model Details
+
+- **Model Name:** Clair-3B
+- **Parameters:** 3 billion
+- **Architecture:** Transformer-based language model
+- **Context Window:** 4,096 tokens
+- **Format:** GGUF (F16)
+- **Size:** 5.75 GB
+
+## Key Features
+
+Clair-3B delivers exceptional performance across a wide range of tasks:
+
+- It possesses **significantly enhanced knowledge** and has greatly improved capabilities in **coding** and **mathematics**, due to specialized training in these domains.
+
+- It demonstrates significant advancements in **instruction following**, **long-text generation**, **understanding structured data** (e.g., tables, JSON), and **generating structured outputs**, especially in JSON format. It is also **highly resilient to diverse system prompts**, improving role-play and condition-setting for chatbots.
+
+- It supports **long contexts** of up to 4,096 tokens and can generate coherent, high-quality responses.
+
+- It offers **multilingual support** for over 29 languages, including English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+### Core Capabilities
+
+-  **Natural Conversation**: Engaging and contextually aware dialogue
+-  **Code Assistance**: Code generation, explanation, debugging, and optimization
+-  **Mathematical Reasoning**: Complex problem solving and step-by-step explanations
+-  **Text Generation**: Creative writing, summarization, and content creation
+-  **Multilingual Support**: Fluent in 29+ languages
+-  **Instruction Following**: Precise adherence to complex instructions and constraints
+-  **Structured Data**: Understanding and generating JSON, tables, and structured formats
+
+## Installation
+
+### Prerequisites
+- [Ollama](https://ollama.com/download) installed on your system
+- At least 6 GB of available RAM (8 GB recommended)
+- Internet connection for initial download
+
+### Quick Install
+
+```bash
+ollama pull r245142r/Clair-3B
+```
+
+### Manual Installation
+
+If you prefer to use a local GGUF file:
+
+1. Download the model file (5.75 GB)
+2. Create a `Modelfile`:
+
+```dockerfile
+FROM ./clair-v4-float16.gguf
+
+SYSTEM """You are Clair, a helpful and friendly AI assistant created by Michael Mlungisi Nkomo from Zimbabwe."""
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER top_k 40
+PARAMETER num_predict 512
+PARAMETER repeat_penalty 1.1
+PARAMETER stop "\n\n"
+PARAMETER stop "User:"
+PARAMETER stop "Human:"
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096
+PARAMETER num_gpu -1
+```
+
+3. Create the model:
+
+```bash
+ollama create clair -f Modelfile
+```
+
+## Usage
+
+### Interactive Chat
+
+```bash
+ollama run r245142r/Clair-3B
+```
+
+Then start chatting:
+
+```
+>>> Can you help me with Python?
+Of course! I'd be happy to help you with Python. What would you like to work on?
+
+>>> Explain recursion with an example
+Recursion is when a function calls itself to solve a problem. Here's a simple factorial example...
+
+>>> Write a function to calculate fibonacci numbers
+Here's an efficient fibonacci function using dynamic programming...
+```
+
+### API Usage
+
+#### REST API
+
+```bash
+curl http://localhost:11434/api/generate -d '{
+  "model": "r245142r/Clair-3B",
+  "prompt": "What is your name and who made you?"
+}'
+```
+
+#### Chat API
+
+```bash
+curl http://localhost:11434/api/chat -d '{
+  "model": "r245142r/Clair-3B",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Tell me about yourself"
+    }
+  ]
+}'
+```
+
+### Python Integration
+
+```python
+import ollama
+
+response = ollama.chat(
+    model='r245142r/Clair-3B',
+    messages=[
+        {
+            'role': 'user',
+            'content': 'What is your name and who made you?'
+        }
+    ]
+)
+
+print(response['message']['content'])
+```
+
+### JavaScript/Node.js Integration
+
+```javascript
+import ollama from 'ollama';
+
+const response = await ollama.chat({
+  model: 'r245142r/Clair-3B',
+  messages: [
+    {
+      role: 'user',
+      content: 'What is your name and who made you?'
+    }
+  ]
+});
+
+console.log(response.message.content);
+```
+
+## Model Parameters
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| `temperature` | 0.7 | Controls randomness (0.0-1.0) |
+| `top_p` | 0.9 | Nucleus sampling threshold |
+| `top_k` | 40 | Limits token selection |
+| `num_predict` | 512 | Maximum tokens to generate |
+| `repeat_penalty` | 1.1 | Penalizes repetitive text |
+| `num_ctx` | 4096 | Context window size |
+| `num_gpu` | -1 | GPU layers (-1 = all) |
+
+### Customizing Parameters
+
+You can override default parameters when running:
+
+```bash
+ollama run r245142r/Clair-3B --temperature 0.5 --num-predict 1024
+```
+
+Or in your Modelfile:
+
+```dockerfile
+PARAMETER temperature 0.5
+PARAMETER num_predict 1024
+```
+
+## Context Window
+
+Clair supports a **4,096 token context window**, which is approximately:
+- 3,000 words of English text
+- 10-15 pages of a typical document
+- 50-100 lines of code
+
+For longer conversations, consider:
+- Summarizing previous context
+- Starting a new conversation
+- Using the `num_ctx` parameter to increase context (requires more RAM)
+
+## Performance
+
+### Hardware Requirements
+
+| Configuration | RAM | GPU | Performance |
+|---------------|-----|-----|-------------|
+| Minimum | 6 GB | None | CPU-only, slower |
+| Recommended | 8 GB | 4+ GB VRAM | GPU-accelerated |
+| Optimal | 16 GB | 8+ GB VRAM | Fast inference |
+
+### Speed Benchmarks
+
+On typical hardware:
+- **CPU-only:** 5-15 tokens/second
+- **GPU-accelerated:** 30-60 tokens/second
+
+## Prompting Best Practices
+
+### For Best Results
+
+1. **Be specific and clear** in your requests
+2. **Provide context** when asking complex questions
+3. **Use examples** to clarify your intent
+4. **Break down complex tasks** into smaller steps
+
+### Example Prompts
+
+**Good:**
+```
+Can you explain how recursion works in Python with a simple example?
+```
+
+**Better:**
+```
+I'm learning Python and struggling with recursion. Can you explain it with a factorial function example and walk me through how it works step by step?
+```
+
+### System Prompts (Optional)
+
+Clair-3B works excellently without system prompts, but you can use them to customize behavior for specific use cases:
+
+```bash
+ollama run r245142r/Clair-3B --system "You are a helpful coding tutor specializing in Python."
+```
+
+Or for different roles:
+
+```bash
+ollama run r245142r/Clair-3B --system "You are a mathematics professor explaining concepts to students."
+```
+
+## Troubleshooting
+
+### Model Not Found
+
+```bash
+# Re-pull the model
+ollama pull r245142r/Clair-3B
+```
+
+### Out of Memory
+
+If you get OOM errors:
+
+1. Close other applications
+2. Reduce context window:
+   ```bash
+   ollama run r245142r/Clair-3B --num-ctx 2048
+   ```
+3. Use CPU-only mode:
+   ```bash
+   ollama run r245142r/Clair-3B --num-gpu 0
+   ```
+
+### Slow Performance
+
+1. Ensure GPU acceleration is enabled
+2. Close other GPU-intensive applications
+3. Consider using a quantized version (Q4_K_M or Q5_K_M) for faster inference
+
+### Model Not Responding Correctly
+
+1. Try a fresh conversation
+2. Clear Ollama cache:
+   ```bash
+   ollama rm r245142r/Clair-3B
+   ollama pull r245142r/Clair-3B
+   ```
+
+## Technical Details
+
+### Model Architecture
+
+Clair-3B is built on a transformer architecture with:
+- 3 billion parameters
+- Optimized for conversational AI
+- Fine-tuned for personality embedding
+
+### Training Data
+
+The model was trained on a diverse dataset including:
+- Conversational data
+- Technical documentation
+- Code examples
+- General knowledge
+- Personality-specific examples
+
+### Quantization Options
+
+While this release uses F16 (full precision), quantized versions are available:
+
+| Format | Size | Quality | Speed |
+|--------|------|---------|-------|
+| F16 | 5.75 GB | Best | Baseline |
+| Q5_K_M | ~2.1 GB | Excellent | Faster |
+| Q4_K_M | ~1.8 GB | Very Good | Fastest |
+| Q3_K_M | ~1.5 GB | Good | Fastest |
+
+## License and Usage
+
+This model is provided for research and personal use. Please respect the creator's work and use responsibly.
+
+## Credits
+
+**Created by:** Michael Mlungisi Nkomo  
+**Location:** Zimbabwe  
+**Project:** Clair AI Assistant
+
+## Support and Community
+
+For issues, questions, or contributions:
+- GitHub: [zim-my repository](https://github.com/Kedarcv/zim-my)
+- Issues: Report bugs or request features on GitHub
+
+## Changelog
+
+### Version 4 (Current)
+- ✅ Personality embedded in model weights
+- ✅ Works without system prompts
+- ✅ Improved identity consistency
+- ✅ Better creator attribution
+- ✅ F16 GGUF format for Ollama
+
+### Version 3
+- Initial LoRA-based implementation
+- Required system prompts for personality
+- Multiple quantization options
+
+## Citation
+
+If you use Clair-3B in your research or projects, please cite:
+
+```bibtex
+@misc{clair3b2026,
+  author = {Michael Mlungisi Nkomo},
+  title = {Clair-3B: An AI Assistant From Zimbabwe},
+  year = {2026},
+  publisher = {Ollama},
+  url = {https://ollama.com/r245142r/Clair-3B}
+}
+```
+
+---
+
+**Note:** This model represents a novel approach to AI personality embedding through weight-level training rather than prompt engineering. The personality and identity are intrinsic to the model, not added through external prompts.

config.json

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+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

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+  "chat_template": "{% macro render_msg(msg) %}{{ msg['role'] }}\n{%- if msg['content'] is string %}\n{{ msg['content'] }}\n{%- elif msg['content'] is iterable %}\n{%- for content in msg['content'] %}\n{%- if content['type'] == 'text' %}\n{{ content['text'] }}\n{%- endif %}\n{%- endfor %}\n{%- endif %}{{ eos_token }}{% endmacro %}{%- if messages[0]['role'] == 'system' -%}{{ render_msg(messages[0]) }}{%- set loop_start = 1 -%}{%- else -%}{%- set loop_start = 0 -%}{%- endif %}{%- for message in messages[loop_start:] %}{% if loop.index0 is even %}user{% else %}assistant{% endif %}\n{{ render_msg(message) }}{%- endfor %}{% if add_generation_prompt %}assistant\n{% endif %}"
+}