Text Generation
Transformers
Safetensors
GGUF
English
qwen2
decompilation
reverse-engineering
python
bytecode
code
verified-generation
conversational
text-generation-inference
Instructions to use BlazingCustoms/pybytecode-v3-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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
- 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
- OpenClaw
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"
| #!/usr/bin/env python3 | |
| """Score a prediction file under BOTH oracles on the SAME benchmark. | |
| PyLingual is OPTIONAL. Without it this still runs and still reports every number that depends | |
| only on our oracle; the columns that need theirs are reported as null with an explicit | |
| `pylingual_available: false`, never silently omitted and never quietly zeroed. | |
| ./dual_oracle.py --gen ../generations/gen_v3_csn.jsonl \\ | |
| --bench ../benchmarks/csn-3.12-licensed/bench.jsonl \\ | |
| --out ../results/dual_ours_csn.json --label "ours / CSN" | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import sys | |
| import tempfile | |
| from pathlib import Path | |
| sys.path.insert(0, str(Path(__file__).resolve().parent)) | |
| from common import ( # noqa: E402 | |
| docstrings_of, load_bench, load_jsonl, ours_ok, pylingual_available, strip_fences, theirs_ok, | |
| ) | |
| def main() -> None: | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--gen", required=True) | |
| ap.add_argument("--bench", required=True) | |
| ap.add_argument("--out", required=True) | |
| ap.add_argument("--label", default="") | |
| a = ap.parse_args() | |
| bench, _ = load_bench(a.bench) | |
| gen = load_jsonl(a.gen) | |
| have_pyl = pylingual_available() | |
| if not have_pyl: | |
| print("NOTE: pylingual is not installed — their-oracle columns will be null. " | |
| "Our-oracle scoring is unaffected. See harness/README.md.", file=sys.stderr) | |
| n = ours = pyl = pyl_ok_ours_fail = 0 | |
| doc_total = doc_recovered = 0 | |
| with tempfile.TemporaryDirectory() as td: | |
| tmp = Path(td) | |
| for g in gen: | |
| i = g["i"] | |
| if i not in bench: | |
| continue | |
| n += 1 | |
| ref = bench[i] | |
| src = strip_fences(g["got"]) | |
| o_ok = ours_ok(src, ref["expected"]) | |
| ours += o_ok | |
| if have_pyl: | |
| p_ok, _ = theirs_ok(src, Path(ref["pyc_path"]), tmp, f"g{i}") | |
| pyl += p_ok | |
| if p_ok and not o_ok: | |
| pyl_ok_ours_fail += 1 | |
| # docstring recovery, over references carrying a REAL docstring | |
| # (the CSN normal form rewrites docstrings to the literal 'pass' — not a real one) | |
| ref_docs = [d for d in docstrings_of(ref["expected"]) if d != "pass"] | |
| if ref_docs: | |
| doc_total += 1 | |
| got = docstrings_of(src) | |
| if all(d in got for d in ref_docs): | |
| doc_recovered += 1 | |
| rep = { | |
| "label": a.label, | |
| "n": n, | |
| "pylingual_available": have_pyl, | |
| "PERFECT_our_oracle_docstring_strict": ours, | |
| "PERFECT_our_oracle_pct": round(100 * ours / max(1, n), 2), | |
| "PERFECT_their_oracle": pyl if have_pyl else None, | |
| "PERFECT_their_oracle_pct": round(100 * pyl / max(1, n), 2) if have_pyl else None, | |
| "passes_THEIRS_but_fails_OURS": pyl_ok_ours_fail if have_pyl else None, | |
| "samples_with_real_docstring": doc_total, | |
| "docstrings_exactly_recovered": doc_recovered, | |
| "docstring_recovery_pct": round(100 * doc_recovered / max(1, doc_total), 2), | |
| } | |
| Path(a.out).parent.mkdir(parents=True, exist_ok=True) | |
| Path(a.out).write_text(json.dumps(rep, indent=2)) | |
| print(json.dumps(rep, indent=2)) | |
| if __name__ == "__main__": | |
| main() | |