Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 - rmsnorm
 - swiglu
 license: apache-2.0
-model_type: lumees-transformer
+model_type: transformer
 datasets:
 - custom-educational-corpus
 metrics:
@@ -25,7 +25,7 @@ widget:
 - text: "In the quiet library, an ancient book began to glow softly, revealing"
   example_title: "Fantasy Literature"
 model-index:
-- name: Lumees-362M
+- name: HK-362M
   results:
   - task:
       type: text-generation
@@ -45,11 +45,11 @@ model-index:
       name: PPL per Million Parameters
 ---
 
-# Lumees-362M
+# HK-362M
 
 ## Model Description
 
-Lumees-362M is a highly efficient 362M parameter transformer model optimized for educational content generation and creative writing. The model achieves breakthrough performance with **5.47 validation perplexity**, representing world-record efficiency in the 300M parameter class.
+HK-362M is a highly efficient 362M parameter transformer model optimized for educational content generation and creative writing. The model achieves breakthrough performance with **5.47 validation perplexity**, representing world-record efficiency in the 300M parameter class.
 
 ### Key Features
 
@@ -76,44 +76,50 @@ Normalization: RMS Normalization
 Activation: SwiGLU
 Dropout: 0.0
 Weight Tying: Yes (embedding and lm_head)
+
 ```
 
 ## Training Details
 
 ### Training Data
-- **Domain**: High quality educational content, scientific materials, creative writing
-- **Languages**: Primarily English with multilingual tokenizer support
-- **Quality**: Tier 1 exceptional quality with manual curation
+
+* **Domain**: High quality educational content, scientific materials, creative writing
+* **Languages**: Primarily English with multilingual tokenizer support
+* **Quality**: Tier 1 exceptional quality with manual curation
 
 ### Training Results
-- **Validation PPL**: 5.47
-- **Training PPL**: 8.43
-- **Training Stability**: Excellent (gradient norm ~0.4)
+
+* **Validation PPL**: 5.47
+* **Training PPL**: 8.43
+* **Training Stability**: Excellent (gradient norm ~0.4)
 
 ## Performance
 
 ### Benchmarks
+
 | Metric | Value | Comparison |
-|--------|-------|------------|
+| --- | --- | --- |
 | Validation Perplexity | 5.47 | 4-5x better than GPT-2 Medium |
 | Parameters | 362M | Similar to GPT-2 Medium (355M) |
 | Efficiency Ratio | 0.0166 PPL/M params | High efficiency |
 
 ### Capabilities
-- **Educational Content**: World-class performance (targeting 3-4 PPL final)
-- **Creative Writing**: Beautiful narrative generation with sophisticated vocabulary
-- **Scientific Communication**: Excellent at explaining complex concepts
-- **Character Development**: Rich character interactions and dialogue
-- **Long-form Coherence**: Maintains coherence across extended sequences
+
+* **Educational Content**: World-class performance (targeting 3-4 PPL final)
+* **Creative Writing**: Beautiful narrative generation with sophisticated vocabulary
+* **Scientific Communication**: Excellent at explaining complex concepts
+* **Character Development**: Rich character interactions and dialogue
+* **Long-form Coherence**: Maintains coherence across extended sequences
 
 ## Usage
 
 ### Direct Usage
+
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("lumees/lumees-362m-base")
-model = AutoModelForCausalLM.from_pretrained("lumees/lumees-362m-base")
+tokenizer = AutoTokenizer.from_pretrained("hasankursun/hk-362m-base")
+model = AutoModelForCausalLM.from_pretrained("hasankursun/hk-362m-base")
 
 # Generate text
 inputs = tokenizer("Once upon a time, in a bustling city where dreams came alive,", return_tensors="pt")
@@ -127,9 +133,11 @@ outputs = model.generate(
 )
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(generated_text)
+
 ```
 
 ### Generation Parameters
+
 ```yaml
 Recommended Settings:
   Temperature: 0.7-0.9
@@ -137,68 +145,76 @@ Recommended Settings:
   Top-k: 40-60
   Repetition Penalty: 1.1
   Max Length: 200-600 tokens
+
 ```
 
 ## Example Generations
 
 ### Creative Storytelling
+
 **Prompt**: "Once upon a time, in a bustling city where dreams came alive,"
 
 **Generation**: "Once upon a time, in a bustling city where dreams came alive, there was an art gallery named 'Pink & Blue.' This quaint little store offered a diverse range of artwork from various artists and eras. Among them were two young friends - Mia, who loved exploring new styles and techniques, and Ben, passionate about capturing nature's beauty on canvas..."
 
 ### Scientific Narrative
+
 **Prompt**: "The scientist looked at the data in disbelief and whispered,"
 
 **Generation**: "The scientist looked at the data in disbelief and whispered, 'This shows how certain conditions can lead to undesirable outcomes.' She then asked, 'But why does that matter if something causes you to get sick?' Mr. Wise Owl explained, 'You see, when we take care of our bodies, especially those living with infectious diseases...'"
 
 ## Limitations
 
-- **Domain Focus**: Optimized for educational/creative content; may underperform on general web text
-- **Context Length**: Current limit of 1024 tokens (extension to 4096+ planned)
-- **Multilingual**: While tokenizer supports 89 languages, model primarily trained on English
-- **Specialized Training**: May require fine-tuning for domains outside educational/creative content
+* **Domain Focus**: Optimized for educational/creative content; may underperform on general web text
+* **Context Length**: Current limit of 1024 tokens (extension to 4096+ planned)
+* **Multilingual**: While tokenizer supports 89 languages, model primarily trained on English
+* **Specialized Training**: May require fine-tuning for domains outside educational/creative content
 
 ## Ethical Considerations
 
 ### Intended Use
-- Educational content generation
-- Creative writing assistance
-- Science communication
-- Research and academic applications
+
+* Educational content generation
+* Creative writing assistance
+* Science communication
+* Research and academic applications
 
 ### Limitations and Biases
-- Training data focused on educational content may introduce domain-specific biases
-- Model should not be used for generating harmful, toxic, or misleading content
-- Outputs should be reviewed for accuracy, especially for factual claims
-- Not suitable for high-stakes decision making without human oversight
+
+* Training data focused on educational content may introduce domain-specific biases
+* Model should not be used for generating harmful, toxic, or misleading content
+* Outputs should be reviewed for accuracy, especially for factual claims
+* Not suitable for high-stakes decision making without human oversight
 
 ## Future Development
 
 This model serves as the foundation for a planned scaling strategy:
-- **724M Model**: Multilingual expansion with general knowledge
-- **1.4B Model**: Global language coverage with advanced capabilities
-- **Context Extension**: RoPE-based scaling to 4096-32768 tokens
+
+* **724M Model**: Multilingual expansion with general knowledge
+* **1.4B Model**: Global language coverage with advanced capabilities
+* **Context Extension**: RoPE-based scaling to 4096-32768 tokens
 
 ## Citation
 
 If you use this model in your research, please cite:
 
 ```bibtex
-@misc{lumees362m2025,
-  title={Lumees-362M: Efficient Domain-Specialized Language Model},
-  author={Hasan KURŞUN and Kerem Berkay YANIK},
+@misc{hk362m2025,
+  title={HK-362M: Efficient Domain-Specialized Language Model},
+  author={Hasan KURŞUN},
   year={2025},
   note={Achieving 5.47 PPL with 362M parameters through strategic domain specialization},
-  url={lumees.io}
+  url={[https://huggingface.co/hasankursun/hk-362m-base](https://huggingface.co/hasankursun/hk-362m-base)}
 }
+
 ```
 
 ## Model Card Authors
 
-- **Developed by**: Hasan KURŞUN,  Kerem Berkay YANIK
-- **Model Type**: Causal Language Model
-- **Language**: English (primary), 89-language tokenizer support
-- **License**: Apache 2.0
-- **Contact**: hello@lumees.io
+* **Developed by**: Hasan KURŞUN
+* **Model Type**: Causal Language Model
+* **Language**: English (primary), 89-language tokenizer support
+* **License**: Apache 2.0
+
+```
 
---- 
+```