Instructions to use BlazingCustoms/pybytecode-v3-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlazingCustoms/pybytecode-v3-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="BlazingCustoms/pybytecode-v3-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BlazingCustoms/pybytecode-v3-1.5b")
model = AutoModelForCausalLM.from_pretrained("BlazingCustoms/pybytecode-v3-1.5b", 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 BlazingCustoms/pybytecode-v3-1.5b 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 BlazingCustoms/pybytecode-v3-1.5b:F16
# Run inference directly in the terminal:
llama cli -hf BlazingCustoms/pybytecode-v3-1.5b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BlazingCustoms/pybytecode-v3-1.5b:F16
# Run inference directly in the terminal:
llama cli -hf BlazingCustoms/pybytecode-v3-1.5b:F16

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 BlazingCustoms/pybytecode-v3-1.5b:F16
# Run inference directly in the terminal:
./llama-cli -hf BlazingCustoms/pybytecode-v3-1.5b:F16

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 BlazingCustoms/pybytecode-v3-1.5b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BlazingCustoms/pybytecode-v3-1.5b:F16

Use Docker

docker model run hf.co/BlazingCustoms/pybytecode-v3-1.5b:F16

LM Studio
Jan

vLLM

How to use BlazingCustoms/pybytecode-v3-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BlazingCustoms/pybytecode-v3-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BlazingCustoms/pybytecode-v3-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BlazingCustoms/pybytecode-v3-1.5b:F16

SGLang

How to use BlazingCustoms/pybytecode-v3-1.5b 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 "BlazingCustoms/pybytecode-v3-1.5b" \
    --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": "BlazingCustoms/pybytecode-v3-1.5b",
		"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 "BlazingCustoms/pybytecode-v3-1.5b" \
        --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": "BlazingCustoms/pybytecode-v3-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use BlazingCustoms/pybytecode-v3-1.5b with Ollama:
```
ollama run hf.co/BlazingCustoms/pybytecode-v3-1.5b:F16
```

Unsloth Studio

How to use BlazingCustoms/pybytecode-v3-1.5b 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 BlazingCustoms/pybytecode-v3-1.5b 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 BlazingCustoms/pybytecode-v3-1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BlazingCustoms/pybytecode-v3-1.5b to start chatting

How to use BlazingCustoms/pybytecode-v3-1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BlazingCustoms/pybytecode-v3-1.5b:F16

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": "BlazingCustoms/pybytecode-v3-1.5b:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use BlazingCustoms/pybytecode-v3-1.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BlazingCustoms/pybytecode-v3-1.5b:F16

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 "BlazingCustoms/pybytecode-v3-1.5b:F16" \
  --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"

Docker Model Runner
How to use BlazingCustoms/pybytecode-v3-1.5b with Docker Model Runner:
```
docker model run hf.co/BlazingCustoms/pybytecode-v3-1.5b:F16
```

Lemonade

How to use BlazingCustoms/pybytecode-v3-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BlazingCustoms/pybytecode-v3-1.5b:F16

Run and chat with the model

lemonade run user.pybytecode-v3-1.5b-F16

List all available models

lemonade list

Hermes Agent

How to use BlazingCustoms/pybytecode-v3-1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BlazingCustoms/pybytecode-v3-1.5b:F16

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 BlazingCustoms/pybytecode-v3-1.5b:F16

Run Hermes

hermes

Atomic Chat

pybytecode-v3-1.5b / NOTICE

coolblaze03

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	PyBytecode
	Copyright 2026 Blazing Customs

	This product includes software developed from Qwen2.5-Coder-1.5B-Instruct
	(https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct), Copyright Alibaba Cloud,
	licensed under the Apache License, Version 2.0.

	The upstream repository ships a LICENSE file and no NOTICE file (checked 2026-08-04 against
	the Hugging Face API), so there is no upstream NOTICE text to append here.

	CHANGES MADE TO THE LICENSED WORK, as required by Apache-2.0 section 4(b):

	The base model was fine-tuned with LoRA (rank 16, alpha 32, applied to q_proj, k_proj,
	v_proj, o_proj, gate_proj, up_proj and down_proj; 1 epoch; learning rate 2e-4) on 48,196
	pairs of Python 3.12 bytecode disassembly and the source that produced it. The resulting
	adapter was merged into the base weights. No architecture, vocabulary or tokenizer change
	was made.

	The training corpus is not distributed. Its per-row attribution was not retained, so it cannot
	be redistributed without stripping required notices from its upstream authors. See
	DATA-CARD-training-corpus.md. This constrains the corpus only; it does not encumber these
	weights, which are trained parameters rather than a copy of any source text, and which were
	derived from a corpus pre-filtered to seven permissive licences with all GPL, LGPL, AGPL, MPL
	and EPL rows dropped before extraction.