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", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop

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 serve -hf SoarAILabs/KiteResolve-20B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SoarAILabs/KiteResolve-20B:Q4_K_M"
        }
      ]
    }
  }
}

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

Hermes Agent

How to use SoarAILabs/KiteResolve-20B with Hermes Agent:

Start the llama.cpp server

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

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SoarAILabs/KiteResolve-20B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SoarAILabs/KiteResolve-20B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

KiteResolve-20B / README.md

aniruddhr04

Update README.md

7f9d583 verified 12 months ago

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	---
	license: apache-2.0
	base_model: openai/gpt-oss-20b
	tags:
	- merge-conflicts
	- git-automation
	- developer-tools
	- code-generation
	- version-control
	- devops
	languages:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	datasets:
	- SoarAILabs/merge-conflict-dataset
	metrics:
	- name: exact_match
	type: exact_match
	value: 22.0
	- name: token_f1
	type: f1
	value: 0.617
	- name: bleu
	type: bleu
	value: 50.82
	- name: rouge-l
	type: rouge
	value: 58.64
	- name: levenshtein_sim
	type: similarity
	value: 0.549
	- name: char_similarity
	type: similarity
	value: 0.765

	model-index:
	- name: KiteResolve-20B
	results:
	- task:
	type: text-generation
	name: Merge Conflict Resolution
	metrics:
	- name: Exact Match
	type: exact_match
	value: 22.0
	- name: Token F1
	type: f1
	value: 0.617
	- name: BLEU
	type: bleu
	value: 50.82
	- name: ROUGE-L
	type: rouge
	value: 58.64
	- name: Levenshtein Similarity
	type: similarity
	value: 0.549
	- name: Character Similarity
	type: similarity
	value: 0.765
	---

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

	Developed by [Soar AI Labs](https://huggingface.co/SoarAILabs)

	<div align="center">
	<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License">
	<img src="https://img.shields.io/badge/Model-20B%20Parameters-red.svg" alt="Parameters">
	<img src="https://img.shields.io/badge/Task-Code%20Generation-green.svg" alt="Task">
	<img src="https://img.shields.io/badge/BLEU-54.83-orange.svg" alt="BLEU Score">
	</div>

	## 🚀 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

	```python
	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 🦙️
	```bash
	ollama run hf.co/SoarAILabs/KiteResolve-20B/model-q4_k_m.gguf
	```