Instructions to use genomalabs/KALYPSO-v1.1L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use genomalabs/KALYPSO-v1.1L with Transformers:

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

pipe = pipeline("text-generation", model="genomalabs/KALYPSO-v1.1L")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("genomalabs/KALYPSO-v1.1L")
model = AutoModelForCausalLM.from_pretrained("genomalabs/KALYPSO-v1.1L", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use genomalabs/KALYPSO-v1.1L 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 genomalabs/KALYPSO-v1.1L:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf genomalabs/KALYPSO-v1.1L:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf genomalabs/KALYPSO-v1.1L:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf genomalabs/KALYPSO-v1.1L: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 genomalabs/KALYPSO-v1.1L:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf genomalabs/KALYPSO-v1.1L: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 genomalabs/KALYPSO-v1.1L:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf genomalabs/KALYPSO-v1.1L:Q4_K_M

Use Docker

docker model run hf.co/genomalabs/KALYPSO-v1.1L:Q4_K_M

LM Studio
Jan

vLLM

How to use genomalabs/KALYPSO-v1.1L with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "genomalabs/KALYPSO-v1.1L"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "genomalabs/KALYPSO-v1.1L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/genomalabs/KALYPSO-v1.1L:Q4_K_M

SGLang

How to use genomalabs/KALYPSO-v1.1L 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 "genomalabs/KALYPSO-v1.1L" \
    --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": "genomalabs/KALYPSO-v1.1L",
		"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 "genomalabs/KALYPSO-v1.1L" \
        --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": "genomalabs/KALYPSO-v1.1L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use genomalabs/KALYPSO-v1.1L with Ollama:
```
ollama run hf.co/genomalabs/KALYPSO-v1.1L:Q4_K_M
```
Unsloth Desktop

How to use genomalabs/KALYPSO-v1.1L with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf genomalabs/KALYPSO-v1.1L: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": "genomalabs/KALYPSO-v1.1L:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use genomalabs/KALYPSO-v1.1L with Docker Model Runner:
```
docker model run hf.co/genomalabs/KALYPSO-v1.1L:Q4_K_M
```

Lemonade

How to use genomalabs/KALYPSO-v1.1L with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull genomalabs/KALYPSO-v1.1L:Q4_K_M

Run and chat with the model

lemonade run user.KALYPSO-v1.1L-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use genomalabs/KALYPSO-v1.1L with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf genomalabs/KALYPSO-v1.1L: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 genomalabs/KALYPSO-v1.1L:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use genomalabs/KALYPSO-v1.1L with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf genomalabs/KALYPSO-v1.1L: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 "genomalabs/KALYPSO-v1.1L: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"

KALYPSO v1.1L

KALYPSO v1.1L is the public coding model of the KALYPSO family by GENOMA Labs. It is Qwen2.5-Coder-14B-Instruct fine-tuned with LoRA (r=64, alpha=128, merged to full fp16 weights) on Kraken-Public, a decontaminated dataset of 18,049 records derived from NVIDIA Nemotron coding data.

It is tuned for agentic coding: multi-step programming tasks, tool-calling scaffolds, and plan-then-implement workflows in Python, C++, and SQL.

Benchmarks (EvalPlus)

Benchmark	KALYPSO v1.1L	Qwen2.5-Coder-14B-Instruct (ref)
HumanEval	87.8	~89
HumanEval+	82.9	~87.2
MBPP	84.7	~84
MBPP+	70.4	~72.8

Honest framing: pure code completion is saturated at the 14B scale, and this model scores at parity with its base. v1.1L is published as the reference fine-tune for the Kraken-Public dataset; its value is agentic breadth, not single-shot completion wins.

Usage

vLLM (recommended for serving):

vllm serve genomalabs/KALYPSO-v1.1L --dtype float16 --max-model-len 32768

Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "genomalabs/KALYPSO-v1.1L"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="float16", device_map="auto")

messages = [{"role": "user", "content": "Write a Python function that merges overlapping intervals."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Uses the standard Qwen2.5 chat template. Context length: 32k.

Family

KALYPSO v1.1L (this model): public 14B dense coder.
genomalabs/kalypso-v1.2l: successor, Qwen3-Coder-30B-A3B based, coding plus planning, with GGUF quants.
Original v1.1L release: GenomaLabs-com/KALYPSO-v1.1L.

Attribution and license

Apache-2.0. Built with Qwen (Qwen2.5-Coder-14B-Instruct, Apache-2.0). Training data derived from NVIDIA Nemotron coding datasets; portions of the data lineage may also be subject to the Qwen and DeepSeek license terms of their originating models.

GENOMA Labs · KALYPSO v1.1L · Built with Qwen

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Safetensors

Model size

15B params

Tensor type

F16

Model tree for genomalabs/KALYPSO-v1.1L

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Finetuned

Qwen/Qwen2.5-Coder-14B-Instruct

Quantized

(132)

this model