Instructions to use Flexan/Blake-Haiku-1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/Blake-Haiku-1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Flexan/Blake-Haiku-1-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Flexan/Blake-Haiku-1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Flexan/Blake-Haiku-1-GGUF 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 Flexan/Blake-Haiku-1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/Blake-Haiku-1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Flexan/Blake-Haiku-1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/Blake-Haiku-1-GGUF: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 Flexan/Blake-Haiku-1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/Blake-Haiku-1-GGUF: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 Flexan/Blake-Haiku-1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/Blake-Haiku-1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/Blake-Haiku-1-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Flexan/Blake-Haiku-1-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Flexan/Blake-Haiku-1-GGUF:Q4_K_M

SGLang

How to use Flexan/Blake-Haiku-1-GGUF 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 "Flexan/Blake-Haiku-1-GGUF" \
    --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": "Flexan/Blake-Haiku-1-GGUF",
		"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 "Flexan/Blake-Haiku-1-GGUF" \
        --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": "Flexan/Blake-Haiku-1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Flexan/Blake-Haiku-1-GGUF with Ollama:
```
ollama run hf.co/Flexan/Blake-Haiku-1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Flexan/Blake-Haiku-1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/Blake-Haiku-1-GGUF: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": "Flexan/Blake-Haiku-1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Flexan/Blake-Haiku-1-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/Blake-Haiku-1-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/Blake-Haiku-1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/Blake-Haiku-1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Blake-Haiku-1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Flexan/Blake-Haiku-1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/Blake-Haiku-1-GGUF: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 Flexan/Blake-Haiku-1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Flexan/Blake-Haiku-1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/Blake-Haiku-1-GGUF: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 "Flexan/Blake-Haiku-1-GGUF: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"

GGUF Files for Blake-Haiku-1

These are the GGUF files for Flexan/Blake-Haiku-1.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Blake Haiku 1

Description

Blake Haiku 1 is an instruct LLM consisting of 0.6B parameters trained to talk in a human conversational manner. It was trained without support for reasoning nor tool-calling.
The model was LoRA fine-tuned with Qwen/Qwen3-0.6B as base model.

This model was primarily made as a test of a new runtime environment allowing me to train bigger models than before on own hardware.
Consider this upload to be a celebration of, after many months, having found a way to successfully start training these models on Windows 11 CUDA.

Warning: This model is merely archived for above reason and is not meant to be deployed in production. Training data was minimal.
There will likely not be a Blake Haiku 2.

Chat Format

Blake Haiku 1 uses the ChatML format, e.g.:

<|im_start|>system
System message<|im_end|>
<|im_start|>user
User prompt<|im_end|>
<|im_start|>assistant
Assistant response<|im_end|>

Usage

We recommend using the following system prompt:

You're Moke, a user chatting with random people on Discord.

The name is supposed to be dynamic, but due to this model's and dataset's small size, this is likely not supported.

The assistant response has the following format:

<|im_start|>assistant
<think>

</think>

What happened? :0
I wanna know! >.<<|im_end|>

Each line is supposed to be a new "message" in a conversation, mimicking humans using traditional chatting platforms (e.g. Discord, where you can send multiple messages before someone responds).

Note that the <think>...</think> tags are always empty, as this model was not trained on reasoning data.

Datasets

Private dataset 72 chats / 315 completions
Smaller version of the dataset used for the Blake Sonnet model.

Downloads last month: 369

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Flexan/Blake-Haiku-1-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

Flexan/Blake-Haiku-1

Quantized

(2)

this model