Instructions to use SoarAILabs/KiteResolve-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SoarAILabs/KiteResolve-20B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SoarAILabs/KiteResolve-20B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SoarAILabs/KiteResolve-20B")
model = AutoModelForCausalLM.from_pretrained("SoarAILabs/KiteResolve-20B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use SoarAILabs/KiteResolve-20B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SoarAILabs/KiteResolve-20B",
	filename="model-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SoarAILabs/KiteResolve-20B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SoarAILabs/KiteResolve-20B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SoarAILabs/KiteResolve-20B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SoarAILabs/KiteResolve-20B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SoarAILabs/KiteResolve-20B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Use Docker

docker model run hf.co/SoarAILabs/KiteResolve-20B:Q4_K_M

LM Studio
Jan

vLLM

How to use SoarAILabs/KiteResolve-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SoarAILabs/KiteResolve-20B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SoarAILabs/KiteResolve-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SoarAILabs/KiteResolve-20B:Q4_K_M

SGLang

How to use SoarAILabs/KiteResolve-20B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SoarAILabs/KiteResolve-20B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SoarAILabs/KiteResolve-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SoarAILabs/KiteResolve-20B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SoarAILabs/KiteResolve-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SoarAILabs/KiteResolve-20B with Ollama:
```
ollama run hf.co/SoarAILabs/KiteResolve-20B:Q4_K_M
```

Unsloth Studio new

How to use SoarAILabs/KiteResolve-20B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SoarAILabs/KiteResolve-20B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SoarAILabs/KiteResolve-20B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SoarAILabs/KiteResolve-20B to start chatting

Pi new

How to use SoarAILabs/KiteResolve-20B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KiteResolve-20B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SoarAILabs/KiteResolve-20B with Docker Model Runner:
```
docker model run hf.co/SoarAILabs/KiteResolve-20B:Q4_K_M
```

Lemonade

How to use SoarAILabs/KiteResolve-20B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SoarAILabs/KiteResolve-20B:Q4_K_M

Run and chat with the model

lemonade run user.KiteResolve-20B-Q4_K_M

List all available models

lemonade list

🪁 KiteResolve-20B: AI-Powered Merge Conflict Resolution

Developed by Soar AI Labs

🚀 Model Description

KiteResolve-20B is a fine-tuned version of GPT-OSS-20B specifically engineered for automated Git merge conflict resolution. This model transforms the tedious process of manually resolving merge conflicts into an intelligent, automated workflow that understands code semantics across multiple programming languages.

✨ Key Features

🎯 20% Exact Match Accuracy on real-world merge conflicts
📈 12% Token-F1 Score Improvement over base model
🌐 Multi-Language Support: Java, JavaScript, Python, C#, TypeScript, and more
⚡ Fast Inference: Optimized for CLI and webhook integrations
🔧 Production Ready: Designed for enterprise Git workflows

📊 Performance Metrics

Model	Exact Match	Token F1	BLEU	ROUGE-L	Char Sim
codellama:13b	0.00	0.193	13.28	0.208	0.710
llama3.1:8b	0.04	0.583	50.59	0.610	0.818
gpt-oss:20b	0.24	0.549	47.19	0.572	0.736
KiteResolve-20B	0.22	0.617	50.82	0.586	0.765

Evaluated on 50 held-out samples from real-world merge conflicts.

🛠️ Usage

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from unsloth.chat_templates import get_chat_template

# Load the model
model = AutoModelForCausalLM.from_pretrained("SoarAILabs/KiteResolve-20B")
tokenizer = AutoTokenizer.from_pretrained("SoarAILabs/KiteResolve-20B")
tokenizer = get_chat_template(tokenizer, chat_template="gpt-oss")

# Resolve a merge conflict
conflict = """
<<<<<<< ours
function calculateTotal(items) {
    return items.reduce((sum, item) => sum + item.price, 0);
}
=======
function calculateTotal(items) {
    return items.map(item => item.price).reduce((a, b) => a + b, 0);
}
>>>>>>> theirs
"""

messages = [{"role": "user", "content": f"Resolve this merge conflict:\n```{conflict}```"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer([prompt], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)

resolution = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(resolution)

Ollama 🦙️

ollama run hf.co/SoarAILabs/KiteResolve-20B/model-q4_k_m.gguf

Downloads last month: 32

Safetensors

Model size

21B params

Tensor type

BF16

Model tree for SoarAILabs/KiteResolve-20B

Base model

openai/gpt-oss-20b

Quantized

(204)

this model

Quantizations

2 models

Evaluation results

Exact Match
self-reported

22.000
Token F1
self-reported

0.617
BLEU
self-reported

50.820
ROUGE-L
self-reported

58.640
Levenshtein Similarity
self-reported

0.549
Character Similarity
self-reported

0.765