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

File size: 170,703 Bytes

ff9936a

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{"i": 41, "repo": "bradmontgomery/django-chosenadmin", "func": "ChosenAdminMiddleware._chosen_css", "commit_sha": "23fad0151d1175d751599a7ee3a1ff35b2d61299", "spdx": "MIT", "n_instr": 38, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 68, "repo": "developersociety/django-glitter", "func": "Reminder.get_interval_timedelta", "commit_sha": "2c0280ec83afee80deee94ee3934fc54239c2e87", "spdx": "BSD-3-Clause", "n_instr": 113, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 69, "repo": "josiah-wolf-oberholtzer/uqbar", "func": "InheritanceGraph._build_mappings", "commit_sha": "eca7fefebbbee1e2ae13bf5d6baa838be66b1db6", "spdx": "MIT", "n_instr": 215, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 75, "repo": "jamescooke/flake8-aaa", "func": "Checker.run", "commit_sha": "29938b96845fe32ced4358ba66af3b3be2a37794", "spdx": "MIT", "n_instr": 117, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 77, "repo": "alixedi/palal", "func": "_stdin_", "commit_sha": "325359f66ac48a9f96efea0489aec353f8a40837", "spdx": "MIT", "n_instr": 28, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 79, "repo": "alixedi/palal", "func": "Survey.format_choices", "commit_sha": "325359f66ac48a9f96efea0489aec353f8a40837", "spdx": "MIT", "n_instr": 72, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 80, "repo": "alixedi/palal", "func": "Survey.is_answer_valid", "commit_sha": "325359f66ac48a9f96efea0489aec353f8a40837", "spdx": "MIT", "n_instr": 47, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 81, "repo": "jamescooke/flake8-aaa", "func": "LineMarkers.update", "commit_sha": "29938b96845fe32ced4358ba66af3b3be2a37794", "spdx": "MIT", "n_instr": 84, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 82, "repo": "jamescooke/flake8-aaa", "func": "LineMarkers.check_arrange_act_spacing", "commit_sha": "29938b96845fe32ced4358ba66af3b3be2a37794", "spdx": "MIT", "n_instr": 54, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 84, "repo": "jamescooke/flake8-aaa", "func": "LineMarkers.check_block_spacing", "commit_sha": "29938b96845fe32ced4358ba66af3b3be2a37794", "spdx": "MIT", "n_instr": 234, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 91, "repo": "bgruening/galaxy_ie_helpers", "func": "_get_ip", "commit_sha": "22d0f77d9e35297e0e6ca4136d5057409699d137", "spdx": "MIT", "n_instr": 63, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 92, "repo": "bgruening/galaxy_ie_helpers", "func": "get_galaxy_connection", "commit_sha": "22d0f77d9e35297e0e6ca4136d5057409699d137", "spdx": "MIT", "n_instr": 127, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 95, "repo": "bgruening/galaxy_ie_helpers", "func": "get_user_history", "commit_sha": "22d0f77d9e35297e0e6ca4136d5057409699d137", "spdx": "MIT", "n_instr": 41, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
{"i": 96, "repo": "quizl/quizler", "func": "get_user_sets", "commit_sha": "44b3fd91f7074e7013ffde8147455f45ebdccc46", "spdx": "MIT", "n_instr": 49, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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{"i": 100, "repo": "MainRo/cyclotron-aio", "func": "run", "commit_sha": "4401076aafe4a72de1d3c4ad6bb7ffa648506f7e", "spdx": "MIT", "n_instr": 38, "tuned_greedy_certified": true, "base_greedy_certified": false, "first_passing_sample": null, "certified_at_32": true}
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