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:

& "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:

& "c:\ai_project\ai_env\Scripts\python.exe" "c:\ai_project\phi2-merged\run_phi2.py" "Write a Python function that reverses a string."