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
- 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
| #!/usr/bin/env python3 | |
| """Grade a prediction file under ONE oracle, with the harness self-tests run first. | |
| ./grade.py --bench <bench.jsonl> --gen <gen.jsonl> --out <report.json> # our oracle | |
| ./grade.py --bench <bench.jsonl> --gen <gen.jsonl> --oracle theirs --out ... # PyLingual's | |
| ./grade.py --bench <bench.jsonl> --self-test-only --out preflight.json | |
| `--oracle ours` needs nothing but the Python standard library. `--oracle theirs` needs the | |
| optional, user-installed PyLingual extra and exits with a clear message if it is absent. | |
| Pre-flight and the mutation test run before any score is computed. If either is not 100% the | |
| command REFUSES to print a score. | |
| """ | |
| 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 | |
| load_bench, load_jsonl, ours_ok, pylingual_available, require_sound, self_test, strip_fences, | |
| theirs_ok, | |
| ) | |
| def main() -> None: | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--bench", required=True) | |
| ap.add_argument("--gen") | |
| ap.add_argument("--out", required=True) | |
| ap.add_argument("--oracle", choices=("ours", "theirs"), default="ours") | |
| ap.add_argument("--label", default="") | |
| ap.add_argument("--self-test-only", action="store_true") | |
| a = ap.parse_args() | |
| if a.oracle == "theirs" and not pylingual_available(): | |
| raise SystemExit( | |
| "--oracle theirs needs the optional PyLingual extra, which is not installed.\n" | |
| "It is GPL-3.0 and is never vendored here; install it yourself (harness/README.md),\n" | |
| "or use --oracle ours, which reproduces every number of ours without it." | |
| ) | |
| bench, _ = load_bench(a.bench) | |
| print(f"self-testing the {a.oracle} oracle on {len(bench)} labels...", file=sys.stderr, flush=True) | |
| st = self_test(bench, a.oracle) | |
| print(f" PRE-FLIGHT {st['preflight_perfect']}/{st['preflight_n']} = {st['preflight_pct']}% " | |
| f"MUTATION {st['mutation_killed']}/{st['mutation_total']} killed = " | |
| f"{st['mutation_kill_rate_pct']}%", file=sys.stderr, flush=True) | |
| require_sound(st) | |
| rep = {"label": a.label, "bench": str(a.bench), **st} | |
| Path(a.out).parent.mkdir(parents=True, exist_ok=True) | |
| if a.self_test_only or not a.gen: | |
| Path(a.out).write_text(json.dumps(rep, indent=2)) | |
| print(json.dumps({k: v for k, v in rep.items() | |
| if k not in ("preflight_failures", "mutation_survivors")}, indent=2)) | |
| return | |
| gen = load_jsonl(a.gen) | |
| n = perfect = 0 | |
| why_counts: dict[str, int] = {} | |
| rows = [] | |
| with tempfile.TemporaryDirectory() as td: | |
| tmp = Path(td) | |
| for g in gen: | |
| i = g["i"] | |
| if i not in bench: | |
| continue | |
| n += 1 | |
| src = strip_fences(g["got"]) | |
| if a.oracle == "ours": | |
| ok = ours_ok(src, bench[i]["expected"]) | |
| why = "PERFECT" if ok else "not byte-identical" | |
| else: | |
| ok, why = theirs_ok(src, Path(bench[i]["pyc_path"]), tmp, f"g{i}") | |
| perfect += ok | |
| if not ok: | |
| key = why.split(":")[0] | |
| why_counts[key] = why_counts.get(key, 0) + 1 | |
| rows.append({"i": i, "perfect": ok, "why": why, | |
| "func": bench[i].get("csn_func", ""), "n_instr": bench[i].get("n_instr")}) | |
| rep.update({ | |
| "gen": str(a.gen), | |
| "scored_n": n, | |
| "PERFECT": perfect, | |
| "PERFECT_pct": round(100 * perfect / max(1, n), 2), | |
| "failure_profile": dict(sorted(why_counts.items(), key=lambda x: -x[1])), | |
| }) | |
| Path(a.out).write_text(json.dumps({**rep, "rows": rows}, indent=2)) | |
| print(json.dumps({k: v for k, v in rep.items() | |
| if k not in ("preflight_failures", "mutation_survivors")}, indent=2)) | |
| if __name__ == "__main__": | |
| main() | |