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kotlarmilos
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Model Details
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kotlarmilos
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microsoft/Phi-4-mini-instruct
Input Modalities
Output Modalities
Supported Functionality
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, changes how a model thinks rather than what it knows. The step after that, the Gemma 4 GlucoLens adapter, 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 to understand the layer underneath all of this.
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.
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.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom 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:"))LoRA configuration
| Parameter | Value |
|---|---|
| Rank r | 8 |
| Alpha | 16 |
| Dropout | 0.05 |
| Target modules | qkv_proj, gate_up_proj |
| Task type | CAUSAL_LM |
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.
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom 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:"))