README.md

---
base_model: microsoft/Phi-4-mini-instruct
base_model_relation: adapter
library_name: peft
pipeline_tag: text-generation
license: mit
language:
  - en
datasets:
  - kotlarmilos/dotnet-runtime
tags:
  - text-generation
  - conversational
  - instruction-tuning
  - domain-adaptation
  - code
  - dotnet
  - csharp
  - lora
  - qlora
  - nf4
  - phi-4
  - peft
  - transformers
  - arxiv:2106.09685
inference: true
---

# Phi-4-mini fine-tuned on dotnet/runtime

This is a LoRA fine-tune of microsoft/Phi-4-mini-instruct adapted to the issue and pull request
history of the dotnet/runtime codebase. The goal is to move a small, efficient base model toward the
vocabulary, conventions, and recurring problems of one specific engineering domain so it reads and
responds in that domain's dialect.

## Where this fits

This is the first step in my applied post-training track. Here I change what a small model knows by
fitting it to a domain I understand. The next step, the [Gemma 3 reasoning
adapter](https://huggingface.co/kotlarmilos/gemma-3-1b-reasoning), changes how a model thinks rather than
what it knows. The step after that, the [Gemma 4
GlucoLens adapter](https://huggingface.co/kotlarmilos/gemma-4-e4b-glucolens), takes the same
instinct into a domain where the output is a structured rollout and the model has to refuse when it
is unsure. In parallel I built a transformer by hand in [gpt2-nano](https://huggingface.co/kotlarmilos/gpt2-nano)
to understand the layer underneath all of this.

## Model details

- Developed by Milos Kotlar
- Base model microsoft/Phi-4-mini-instruct
- Method LoRA with 4-bit NF4 quantization
- Language English
- License MIT
- Repository https://github.com/kotlarmilos/phi-4-mini-dotnet-runtime
- Demo https://huggingface.co/spaces/kotlarmilos/dotnet-runtime

## Intended use

The model is meant for assistance on the dotnet/runtime domain, reading issues and pull requests and
drafting responses in the terminology and style of that codebase. It is a research artifact, not a
production reviewer.

## Out of scope

The model is not built for factual retrieval, and it can produce plausible but wrong statements. It
is not a source of professional medical or legal advice, and it is not suitable for safety critical
systems. Do not use it to generate harmful or misleading content.

## How to load

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

BASE    = "microsoft/Phi-4-mini-instruct"
ADAPTER = "kotlarmilos/phi-4-mini-dotnet-runtime"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(BASE, use_fast=True)
base = AutoModelForCausalLM.from_pretrained(
    BASE, quantization_config=bnb_config, device_map="auto", trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, ADAPTER)

def generate(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(
        **inputs, max_new_tokens=256, do_sample=True, temperature=0.7,
        pad_token_id=tokenizer.eos_token_id,
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

print(generate("Review the following code changes:"))
```

## Training

- Data. About 10,000 instruction and response pairs built from dotnet/runtime GitHub issues and pull
  requests, published as the [dotnet-runtime dataset](https://huggingface.co/datasets/kotlarmilos/dotnet-runtime).
- Method. LoRA on the quantized base model, mixed precision.
- Quantization. 4-bit NF4 with BitsAndBytes.

**LoRA configuration**

| Parameter | Value |
|---|---|
| Rank r | 8 |
| Alpha | 16 |
| Dropout | 0.05 |
| Target modules | `qkv_proj`, `gate_up_proj` |
| Task type | CAUSAL_LM |

## Evaluation

I do not report a held-out benchmark score for this model. The effect of fine-tuning is a shift
toward the repository's terminology and issue framing relative to the base model on the same prompts.
A labeled evaluation split drawn from held-out issues is the natural next step. See the repository for
details.

## Source

- Repository https://github.com/kotlarmilos/phi-4-mini-dotnet-runtime
- Dataset https://huggingface.co/datasets/kotlarmilos/dotnet-runtime
- Demo https://huggingface.co/spaces/kotlarmilos/dotnet-runtime
- Writeup https://huggingface.co/blog/kotlarmilos/phi-4-mini-dotnet-runtime