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"
| def plotcommand(cosmology='WMAP5', plotname=None): | |
| """pass""" | |
| xarray = 10 ** np.arange(1, 15, 0.2) | |
| yval = 'c' | |
| zarray = np.arange(0, 5, 0.5) | |
| xtitle = 'Halo Mass (M$_{sol}$)' | |
| ytitle = 'Concentration' | |
| linelabel = 'z=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| plt.ylim([2, 30]) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=zval, Mi=xarray) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray, label=linelabel + str(zval), color=colors[zind]) | |
| ax.plot(xarray, commah.commah.cduffy(zval, xarray), color='black') | |
| ax.set_xscale('log') | |
| ax.set_yscale('log') | |
| leg = ax.legend(loc=1) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_CM_relation.png'" % plotname) | |
| fig.savefig(plotname + '_CM_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(0, 1, 0.05) - 1 | |
| yval = 'c' | |
| zarray = 10 ** np.arange(6, 14, 2) | |
| xtitle = 'Redshift' | |
| ytitle = 'NFW Concentration' | |
| linelabel = 'log$_{10}$ M$_{z}$(M$_{sol}$)=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=xarray, Mi=zval) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray, label=linelabel + '{0:.1f}'.format(np.log10(zval)), color=colors[zind]) | |
| leg = ax.legend(loc=1) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_Cz_relation.png'" % plotname) | |
| fig.savefig(plotname + '_Cz_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(0, 1, 0.05) - 1 | |
| yval = 'zf' | |
| zarray = 10 ** np.arange(6, 14, 2) | |
| xtitle = 'Redshift' | |
| ytitle = 'Formation Redshift' | |
| linelabel = 'log$_{10}$ M$_{z}$(M$_{sol}$)=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=xarray, Mi=zval) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray, label=linelabel + '{0:.1f}'.format(np.log10(zval)), color=colors[zind]) | |
| leg = ax.legend(loc=2) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_zfz_relation.png'" % plotname) | |
| fig.savefig(plotname + '_zfz_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(0, 1, 0.05) - 1 | |
| yval = 'dMdt' | |
| zarray = 10 ** np.arange(10, 14, 0.5) | |
| xtitle = 'log$_{10}$ (1+z)' | |
| ytitle = 'log$_{10}$ Accretion Rate M$_{sol}$ yr$^{-1}$' | |
| linelabel = 'log$_{10}$ M$_z$(M$_{sol}$)=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| cosmo = commah.getcosmo(cosmology) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=xarray, Mi=zval, com=False, mah=True) | |
| yarray = output[yval].flatten() | |
| ax.plot(np.log10(xarray + 1.0), np.log10(yarray), label=linelabel + '{0:.1f}'.format(np.log10(zval)), color=colors[zind]) | |
| semianalytic_approx = 71.6 * (zval / 1000000000000.0) * (cosmo['h'] / 0.7) * (-0.24 + 0.75 * (xarray + 1)) * np.sqrt(cosmo['omega_M_0'] * (xarray + 1) ** 3 + cosmo['omega_lambda_0']) | |
| ax.plot(np.log10(xarray + 1), np.log10(semianalytic_approx), color='black') | |
| leg = ax.legend(loc=2) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_dMdtz_relation.png'" % plotname) | |
| fig.savefig(plotname + '_dMdtz_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(10, 14, 0.5) | |
| yval = 'dMdt' | |
| zarray = np.arange(0, 5, 0.5) | |
| xtitle = 'Halo Mass M$_{sol}$' | |
| ytitle = 'Accretion Rate M$_{sol}$ yr$^{-1}$' | |
| linelabel = 'z=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=zval, Mi=xarray, com=False, mah=True) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray, label=linelabel + str(zval), color=colors[zind]) | |
| ax.set_xscale('log') | |
| ax.set_yscale('log') | |
| leg = ax.legend(loc=2) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_MAH_M_relation.png'" % plotname) | |
| fig.savefig(plotname + '_MAH_M_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(10, 14, 0.5) | |
| yval = 'dMdt' | |
| zarray = np.arange(0, 5, 0.5) | |
| xtitle = 'Halo Mass M$_{sol}$' | |
| ytitle = 'Specific Accretion Rate yr$^{-1}$' | |
| linelabel = 'z=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=zval, Mi=xarray, mah=True, com=False) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray / xarray, label=linelabel + str(zval), color=colors[zind]) | |
| ax.set_xscale('log') | |
| ax.set_yscale('log') | |
| leg = ax.legend(loc=1) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_specificMAH_M_relation.png'" % plotname) | |
| fig.savefig(plotname + '_specificMAH_M_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(0, 1, 0.05) - 1 | |
| yval = 'Mz' | |
| zarray = 10 ** np.arange(10, 14, 0.5) | |
| xtitle = 'Redshift' | |
| ytitle = 'M(z) (M$_{sol}$)' | |
| linelabel = 'log$_{10}$ M$_{0}$(M$_{sol}$)=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=0, Mi=zval, z=xarray) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, yarray, label=linelabel + '{0:.1f}'.format(np.log10(zval)), color=colors[zind]) | |
| ax.set_yscale('log') | |
| leg = ax.legend(loc=1) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_Mzz_relation.png'" % plotname) | |
| fig.savefig(plotname + '_Mzz_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| xarray = 10 ** np.arange(0, 1, 0.02) - 1 | |
| yval = 'Mz' | |
| zarray = 10 ** np.arange(10, 14, 0.5) | |
| xtitle = 'Redshift' | |
| ytitle = 'log$_{10}$ M(z)/M$_{0}$' | |
| linelabel = 'log$_{10}$ M$_{0}$(M$_{sol}$)=' | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111) | |
| ax.set_xlabel(xtitle) | |
| ax.set_ylabel(ytitle) | |
| colors = cm.rainbow(np.linspace(0, 1, len(zarray))) | |
| for zind, zval in enumerate(zarray): | |
| output = commah.run(cosmology=cosmology, zi=0, Mi=zval, z=xarray) | |
| yarray = output[yval].flatten() | |
| ax.plot(xarray, np.log10(yarray / zval), label=linelabel + '{0:.1f}'.format(np.log10(zval)), color=colors[zind]) | |
| leg = ax.legend(loc=3) | |
| leg.get_frame().set_alpha(0) | |
| leg.get_frame().set_edgecolor('white') | |
| for label in leg.get_texts(): | |
| label.set_fontsize('small') | |
| for label in leg.get_lines(): | |
| label.set_linewidth(4) | |
| if plotname: | |
| fig.tight_layout(pad=0.2) | |
| print("Plotting to '%s_MzM0z_relation.png'" % plotname) | |
| fig.savefig(plotname + '_MzM0z_relation.png', dpi=fig.dpi * 5) | |
| else: | |
| plt.show() | |
| return 'Done' |