| # phi2-merged Model Report | |
| ## Summary | |
| This folder contains a standalone causal language model with a Phi family architecture: | |
| - `architectures`: `PhiForCausalLM` | |
| - `model_type`: `phi` | |
| - `hidden_size`: `2560` | |
| - `num_hidden_layers`: `32` | |
| - `num_attention_heads`: `32` | |
| - `vocab_size`: `51200` | |
| The weight file is a full model checkpoint, not a lightweight adapter. The safetensors keys are all standard backbone parameters such as `model.layers.*`, `model.embed_tokens.weight`, and `lm_head.*`. There are no LoRA, adapter, prefix-tuning, or chat-template artifacts in the checkpoint layout. | |
| ## Base Model or Instruct Model | |
| Best classification from the local evidence: this is a base causal LM, not a clearly packaged instruct/chat model. | |
| Why: | |
| - The config does not declare an instruct or chat variant. | |
| - The tokenizer files do not define a special instruction/chat template. | |
| - The checkpoint layout is a plain full model checkpoint. | |
| - Behavior is mixed: it answers simple prompts and code requests well, but it also repeats or drifts on some Turkish chat-style prompts instead of consistently following a conversational instruction format. | |
| ## What It Looks Fine-Tuned For | |
| The model appears strongest in the following areas: | |
| - short factual completions and prompt continuation | |
| - simple arithmetic and reasoning-style prompts | |
| - code generation, especially small Python snippets | |
| - English-language instructions better than Turkish chat formatting | |
| The generated outputs suggest some instruction-following ability, but not the stronger, more stable behavior typical of a dedicated chat-tuned model. | |
| ## What It Is Better At | |
| Based on the local probes run in the terminal, the model seems better at: | |
| - direct, narrowly scoped tasks | |
| - code answers with obvious structure | |
| - math-style completions | |
| - continuation after explicit answer cues such as `Answer:` or `Cevap:` | |
| It seems weaker at: | |
| - multi-turn conversational flow | |
| - Turkish dialogue formatting | |
| - avoiding repetition when the prompt is loosely structured | |
| ## Evidence From Local Probes | |
| Observed behavior: | |
| - For a Turkish math prompt, it produced a correct `2 + 2 = 4` style answer, but then kept extending into repetitive or mixed reasoning. | |
| - For a Turkish chat prompt, it echoed the prompt content instead of cleanly producing a single assistant reply. | |
| - For an English coding prompt, it produced a clean Python function to reverse a string. | |
| ## Recommended Usage | |
| - Use it as a general causal LM or a prompt-completion model. | |
| - Prefer explicit answer cues like `Answer:` or `Cevap:`. | |
| - For chat usage, wrap it with a custom prompt format if you want more stable assistant-style responses. | |
| ## Run Command | |
| After activating the upper-level virtual environment, run: | |
| ```powershell | |
| & "c:\ai_project\ai_env\Scripts\python.exe" "c:\ai_project\phi2-merged\run_phi2.py" "Kısa bir selam ver:" | |
| ``` | |
| You can also pass a custom prompt: | |
| ```powershell | |
| & "c:\ai_project\ai_env\Scripts\python.exe" "c:\ai_project\phi2-merged\run_phi2.py" "Write a Python function that reverses a string." | |
| ``` |