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 / harness /config.py

coolblaze03

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0b19a1b verified 6 days ago

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	#!/usr/bin/env python3
	"""Everything environment-specific, in one place.

	The first version of this harness hard-coded absolute build-machine paths, a scratch directory
	under /tmp, `http://localhost:1234/v1` and one model name, so nobody else could run it: measured
	2026-08-04, 46 such lines across 22 scripts, plus 679 benchmark rows whose `pyc_path` pointed into
	a scratch directory that no longer exists. Nothing here is hard-coded to a machine: paths are
	resolved relative to this file, and the model endpoint comes from the environment.

	PYBYTECODE_ENDPOINT OpenAI-compatible base URL (default http://localhost:1234/v1)
	PYBYTECODE_MODEL model name at that endpoint (default pybytecode-v3-1.5b)
	PYBYTECODE_API_KEY bearer token, if the server wants one (default "not-needed")

	Only `generate.py` reads the endpoint. Every grading path is offline: given the cached
	generations in this repository, all published numbers reproduce with no model and no GPU.
	"""
	from __future__ import annotations

	import os
	from pathlib import Path

	HARNESS_DIR = Path(__file__).resolve().parent
	RELEASE_DIR = HARNESS_DIR.parent
	BENCHMARKS_DIR = RELEASE_DIR / "benchmarks"
	GENERATIONS_DIR = RELEASE_DIR / "generations"
	RESULTS_DIR = RELEASE_DIR / "results"

	ENDPOINT = os.environ.get("PYBYTECODE_ENDPOINT", "http://localhost:1234/v1")
	MODEL = os.environ.get("PYBYTECODE_MODEL", "pybytecode-v3-1.5b")
	API_KEY = os.environ.get("PYBYTECODE_API_KEY", "not-needed")


	def resolve_bench_asset(bench_file: Path, stored_path: str, kind: str, index: int) -> Path:
	"""Resolve a row's .pyc / .py path RELATIVE TO ITS OWN bench.jsonl.

	Benchmarks built by the original harness stored absolute scratch-directory paths, which do
	not exist on any other machine (and no longer exist on the build machine either). The files
	themselves have always been committed next to bench.jsonl, so the stored string is ignored
	whenever the co-located file exists. A stored RELATIVE path is honoured as written.

	`kind` is "pyc" or "src"; `index` is the row's `i`.
	"""
	bench_dir = bench_file.resolve().parent
	if stored_path and not stored_path.startswith("/"):
	p = bench_dir / stored_path
	if p.exists():
	return p
	ext = ".pyc" if kind == "pyc" else ".py"
	p = bench_dir / kind / f"{index:05d}{ext}"
	if p.exists():
	return p
	# last resort: trust the stored absolute path, so a failure names the real missing file
	return Path(stored_path) if stored_path else p


	def have_pylingual() -> bool:
	"""PyLingual is an OPTIONAL, user-installed extra. It is GPL-3.0 and is never vendored."""
	try:
	import pylingual.equivalence_check # noqa: F401
	return True
	except Exception: # noqa: BLE001
	return False