Upload LUMI-trained GPT-Neo 1.3B with code and conversation capabilities

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- conversational-ai
+- code-generation
+- python
+- gpt-neo
+- instruction-following
+metrics:
+- name: Training Loss (Final)
+  type: loss
+  value: 0.4554
+  verified: false
+- name: Dataset Size
+  type: examples
+  value: 362059
+  verified: false
+---
+
+# GPT-Neo 1.3B Enhanced for Code and Conversation
+
+A fine-tuned version of GPT-Neo 1.3B optimized for both conversational AI and Python code generation. This model combines instruction-following capabilities with comprehensive Python programming knowledge.
+
+## Model Description
+
+This model represents a multi-layer fine-tuning approach:
+- **Base**: EleutherAI's GPT-Neo 1.3B
+- **Layer 1**: Conversational fine-tuning for instruction-following
+- **Layer 2**: Python code generation using CodeSearchNet dataset (362,059 examples)
+
+## Training Details
+
+- **Architecture**: GPT-Neo 1.3B (transformer-based autoregressive language model)
+- **Training Data**: High-quality Python code examples with documentation
+- **Training Infrastructure**: European HPC systems with AMD GPU acceleration
+- **Optimization**: Multi-GPU distributed training with gradient accumulation
+- **Final Training Loss**: 0.4554 (excellent convergence)
+
+## Usage
+
+### Code Generation
+```python
+from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+
+model = GPTNeoForCausalLM.from_pretrained("your-username/gpt-neo-1.3b-code-conversation")
+tokenizer = GPT2Tokenizer.from_pretrained("your-username/gpt-neo-1.3b-code-conversation")
+tokenizer.pad_token = tokenizer.eos_token
+
+# Code generation example
+prompt = "Human: Write a Python function that calculates the factorial of a number\nAssistant:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+Conversational AI
+python# Conversation example
+prompt = "Human: Explain machine learning in simple terms\nAssistant:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=150, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+Training Methodology
+The model was trained using a proven multi-layer approach:
+
+Conversational Foundation: Initial fine-tuning on high-quality conversation data
+Code Specialization: Subsequent training on curated Python programming examples
+Quality Filtering: Rigorous filtering for meaningful code-documentation pairs
+Distributed Training: Efficient scaling across multiple GPUs
+
+Performance Characteristics
+
+Code Understanding: Strong comprehension of Python syntax and patterns
+Documentation: Ability to explain code functionality clearly
+Instruction Following: Responds appropriately to programming requests
+Conversational Flow: Maintains context in multi-turn interactions
+
+Model Capabilities
+Code Generation
+
+Python functions with proper documentation
+Algorithm implementations
+Data structure manipulations
+Error handling patterns
+
+Conversational AI
+
+Technical explanations
+Step-by-step instructions
+Problem-solving discussions
+Educational content
+
+Limitations
+
+Primarily trained on Python code (limited other languages)
+May generate plausible but incorrect code for complex tasks
+Training data cutoff affects knowledge of recent libraries
+Best results with clear, specific prompts
+
+Ethical Considerations
+
+Model outputs should be reviewed for production use
+Code suggestions require testing and validation
+Potential for generating biased or inappropriate content
+Users responsible for compliance with applicable regulations
+
+Citation
+bibtex@misc{gpt-neo-code-conversation-2025,
+  title={GPT-Neo 1.3B Enhanced for Code and Conversation},
+  author={Your Name},
+  year={2025},
+  howpublished={Hugging Face Model Hub},
+  url={https://huggingface.co/your-username/gpt-neo-1.3b-code-conversation}
+}
+Acknowledgments
+Training conducted using European high-performance computing infrastructure. Based on EleutherAI's GPT-Neo and CodeSearchNet dataset.

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPTNeoForCausalLM"
+  ],
+  "attention_dropout": 0,
+  "attention_layers": [
+    "global",
+    "local",
+    "global",
+    "local",
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+    "local",
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+    "local",
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+    "local",
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+    "local",
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+    "local",
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+    "local",
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+    "local",
+    "global",
+    "local",
+    "global",
+    "local"
+  ],
+  "attention_types": [
+    [
+      [
+        "global",
+        "local"
+      ],
+      12
+    ]
+  ],
+  "bos_token_id": 50256,
+  "classifier_dropout": 0.1,
+  "embed_dropout": 0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": null,
+  "layer_norm_epsilon": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neo",
+  "num_heads": 16,
+  "num_layers": 24,
+  "resid_dropout": 0,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50,
+      "temperature": 0.9
+    }
+  },
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.3",
+  "use_cache": true,
+  "vocab_size": 50257,
+  "window_size": 256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.52.3"
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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