File size: 3,122 Bytes
0a25951 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # 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."
``` |