Text Generation
Transformers
Safetensors
GGUF
PEFT
English
qwen3_moe
angular
ollama
typescript
frontend
code
lora
conversational
Instructions to use bharathmuppa/angular-coder-30b-a3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bharathmuppa/angular-coder-30b-a3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bharathmuppa/angular-coder-30b-a3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bharathmuppa/angular-coder-30b-a3b") model = AutoModelForCausalLM.from_pretrained("bharathmuppa/angular-coder-30b-a3b", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - PEFT
How to use bharathmuppa/angular-coder-30b-a3b with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bharathmuppa/angular-coder-30b-a3b 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 bharathmuppa/angular-coder-30b-a3b:Q4_K_M # Run inference directly in the terminal: llama cli -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M # Run inference directly in the terminal: llama cli -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
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 bharathmuppa/angular-coder-30b-a3b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
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 bharathmuppa/angular-coder-30b-a3b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
Use Docker
docker model run hf.co/bharathmuppa/angular-coder-30b-a3b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bharathmuppa/angular-coder-30b-a3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bharathmuppa/angular-coder-30b-a3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bharathmuppa/angular-coder-30b-a3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bharathmuppa/angular-coder-30b-a3b:Q4_K_M
- SGLang
How to use bharathmuppa/angular-coder-30b-a3b 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 "bharathmuppa/angular-coder-30b-a3b" \ --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": "bharathmuppa/angular-coder-30b-a3b", "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 "bharathmuppa/angular-coder-30b-a3b" \ --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": "bharathmuppa/angular-coder-30b-a3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use bharathmuppa/angular-coder-30b-a3b with Ollama:
ollama run hf.co/bharathmuppa/angular-coder-30b-a3b:Q4_K_M
- Unsloth Desktop
- Pi
How to use bharathmuppa/angular-coder-30b-a3b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bharathmuppa/angular-coder-30b-a3b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bharathmuppa/angular-coder-30b-a3b with Docker Model Runner:
docker model run hf.co/bharathmuppa/angular-coder-30b-a3b:Q4_K_M
- Lemonade
How to use bharathmuppa/angular-coder-30b-a3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bharathmuppa/angular-coder-30b-a3b:Q4_K_M
Run and chat with the model
lemonade run user.angular-coder-30b-a3b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use bharathmuppa/angular-coder-30b-a3b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
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 bharathmuppa/angular-coder-30b-a3b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bharathmuppa/angular-coder-30b-a3b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bharathmuppa/angular-coder-30b-a3b:Q4_K_M
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 "bharathmuppa/angular-coder-30b-a3b:Q4_K_M" \ --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"
Download chat_template.jinja from bharathmuppa/angular-coder-30b-a3b: direct link, hf CLI and curl.
- Browser
- Download file 6.21 kB
-
https://huggingface.co/bharathmuppa/angular-coder-30b-a3b/resolve/main/chat_template.jinja
- Command line
-
hf download hf://bharathmuppa/angular-coder-30b-a3b/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/bharathmuppa/angular-coder-30b-a3b/resolve/main/chat_template.jinja
6.21 kB
| {% macro render_extra_keys(json_dict, handled_keys) %} | |
| {%- if json_dict is mapping %} | |
| {%- for json_key in json_dict if json_key not in handled_keys %} | |
| {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %} | |
| {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }} | |
| {%- else %} | |
| {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- endif %} | |
| {% endmacro %} | |
| {%- if messages[0]["role"] == "system" %} | |
| {%- set system_message = messages[0]["content"] %} | |
| {%- set loop_messages = messages[1:] %} | |
| {%- else %} | |
| {%- set loop_messages = messages %} | |
| {%- endif %} | |
| {%- if not tools is defined %} | |
| {%- set tools = [] %} | |
| {%- endif %} | |
| {%- if system_message is defined %} | |
| {{- "<|im_start|>system\n" + system_message }} | |
| {%- else %} | |
| {%- if tools is iterable and tools | length > 0 %} | |
| {{- "<|im_start|>system\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks." }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if tools is iterable and tools | length > 0 %} | |
| {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }} | |
| {{- "<tools>" }} | |
| {%- for tool in tools %} | |
| {%- if tool.function is defined %} | |
| {%- set tool = tool.function %} | |
| {%- endif %} | |
| {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }} | |
| {%- if tool.description is defined %} | |
| {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }} | |
| {%- endif %} | |
| {{- '\n<parameters>' }} | |
| {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %} | |
| {%- for param_name, param_fields in tool.parameters.properties|items %} | |
| {{- '\n<parameter>' }} | |
| {{- '\n<name>' ~ param_name ~ '</name>' }} | |
| {%- if param_fields.type is defined %} | |
| {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }} | |
| {%- endif %} | |
| {%- if param_fields.description is defined %} | |
| {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }} | |
| {%- endif %} | |
| {%- set handled_keys = ['name', 'type', 'description'] %} | |
| {{- render_extra_keys(param_fields, handled_keys) }} | |
| {{- '\n</parameter>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {% set handled_keys = ['type', 'properties'] %} | |
| {{- render_extra_keys(tool.parameters, handled_keys) }} | |
| {{- '\n</parameters>' }} | |
| {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} | |
| {{- render_extra_keys(tool, handled_keys) }} | |
| {{- '\n</function>' }} | |
| {%- endfor %} | |
| {{- "\n</tools>" }} | |
| {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }} | |
| {%- endif %} | |
| {%- if system_message is defined %} | |
| {{- '<|im_end|>\n' }} | |
| {%- else %} | |
| {%- if tools is iterable and tools | length > 0 %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- for message in loop_messages %} | |
| {%- if message.role == "assistant" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %} | |
| {{- '<|im_start|>' + message.role }} | |
| {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %} | |
| {{- '\n' + message.content | trim + '\n' }} | |
| {%- endif %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }} | |
| {%- if tool_call.arguments is defined %} | |
| {%- for args_name, args_value in tool_call.arguments|items %} | |
| {{- '<parameter=' + args_name + '>\n' }} | |
| {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} | |
| {{- args_value }} | |
| {{- '\n</parameter>\n' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '</function>\n</tool_call>' }} | |
| {%- endfor %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == "user" or message.role == "system" or message.role == "assistant" %} | |
| {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if loop.previtem and loop.previtem.role != "tool" %} | |
| {{- '<|im_start|>user\n' }} | |
| {%- endif %} | |
| {{- '<tool_response>\n' }} | |
| {{- message.content }} | |
| {{- '\n</tool_response>\n' }} | |
| {%- if not loop.last and loop.nextitem.role != "tool" %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif loop.last %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- endif %} | |