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README.md

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+---
+language:
+- tr
+- en
+license: mit
+tags:
+- phi-2
+- microsoft
+- text-generation
+- tr
+- turkish
+- qlora
+inference: false
+pipeline_tag: text-generation
+---
+
+# sixfinger-phi2-merged
+
+This model is a fine-tuned and merged version of [Microsoft Phi-2](https://huggingface.co/microsoft/phi-2) created by **Six Finger Dev** (Enes Altıparmak). It is a 2.7 billion parameter causal language model tailored to perform well on Turkish Question-Answering (QA), reasoning, and basic coding tasks.
+
+## Model Details
+
+- **Developer:** Six Finger Dev (Enes Altıparmak - Kayseri Science High School)
+- **Architecture:** Phi-2 Causal LM
+- **Parameters:** ~2.7B
+- **Languages:** Turkish (TR), English (EN)
+- **License:** MIT
+
+## Training & Optimization
+This model was likely fine-tuned using QLoRA against a custom Turkish instruction and multi-turn QA dataset (e.g., [sixfingerdev/turkish-qa-multi-dialog-dataset](https://huggingface.co/datasets/sixfingerdev/turkish-qa-multi-dialog-dataset)). After fine-tuning, the PEFT adapters were fully merged back into the base model weights, meaning it can be loaded directly as a standalone checkpoint without needing the base model or adapter configuration.
+
+## Usage
+
+You can load and generate text with this model directly using the `transformers` library:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "sixfingerdev/sixfinger-phi2-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True
+)
+
+prompt = "Soru: Türkiyenin başkenti neresidir? Cevap:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=40)
+    
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations & Biases
+While fine-tuned with instruction data, its behavior still heavily relies on prompt-completion formatting. Direct cues like `Answer:` or `Cevap:` yield the best deterministic outputs. In unstructured or lengthy multi-turn chat loops, the model may suffer from repetition or formatting drift compared to purely conversational templates.

added_tokens.json

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+{
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+  "\t\t\t": 50293,
+  "\t\t\t\t": 50292,
+  "\t\t\t\t\t": 50291,
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+  "\t\t\t\t\t\t\t": 50289,
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+  "\t\t\t\t\t\t\t\t\t": 50287,
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+  "                           ": 50261,
+  "                            ": 50260,
+  "                             ": 50259,
+  "                              ": 50258,
+  "                               ": 50257
+}

config.json

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+{
+  "architectures": [
+    "PhiForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 50256,
+  "dtype": "float16",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "hidden_act": "gelu_new",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "phi",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "partial_rotary_factor": 0.4,
+  "qk_layernorm": false,
+  "resid_pdrop": 0.1,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 51200
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9bf11f8a66f74b423602c3259ea7c2c6e9f9a6378b27adf738533d4f2361db99
+size 5559417456

model_report.md

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+# 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."
+```

run_phi2.py

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+import argparse
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Run the local phi2-merged model.")
+    parser.add_argument(
+        "prompt",
+        nargs="?",
+        default="Kısa bir selam ver:",
+        help="Prompt to send to the model.",
+    )
+    parser.add_argument(
+        "--max-new-tokens",
+        type=int,
+        default=120,
+        help="Maximum number of new tokens to generate.",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.0,
+        help="Sampling temperature. Default 0 uses deterministic greedy decoding.",
+    )
+    parser.add_argument(
+        "--top-p",
+        type=float,
+        default=0.9,
+        help="Nucleus sampling threshold.",
+    )
+    return parser
+
+
+def main() -> None:
+    args = build_parser().parse_args()
+    model_path = Path(__file__).resolve().parent
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        dtype=dtype,
+        device_map="auto" if torch.cuda.is_available() else None,
+        low_cpu_mem_usage=True,
+    )
+
+    inputs = tokenizer(args.prompt, return_tensors="pt")
+    inputs = {name: tensor.to(model.device) for name, tensor in inputs.items()}
+
+    generate_kwargs = {
+        "max_new_tokens": args.max_new_tokens,
+        "pad_token_id": tokenizer.eos_token_id,
+    }
+    if args.temperature > 0:
+        generate_kwargs["do_sample"] = True
+        generate_kwargs["temperature"] = args.temperature
+        generate_kwargs["top_p"] = args.top_p
+    else:
+        generate_kwargs["do_sample"] = False
+
+    with torch.no_grad():
+        output = model.generate(**inputs, **generate_kwargs)
+
+    print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+
+if __name__ == "__main__":
+    main()

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+}