Text Generation
Transformers
Safetensors
English
llama
llama-3
meta
facebook
unsloth
conversational
text-generation-inference
Instructions to use baseten/Llama-3.2-3B-Instruct-pythonic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseten/Llama-3.2-3B-Instruct-pythonic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="baseten/Llama-3.2-3B-Instruct-pythonic") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("baseten/Llama-3.2-3B-Instruct-pythonic") model = AutoModelForCausalLM.from_pretrained("baseten/Llama-3.2-3B-Instruct-pythonic") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use baseten/Llama-3.2-3B-Instruct-pythonic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "baseten/Llama-3.2-3B-Instruct-pythonic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baseten/Llama-3.2-3B-Instruct-pythonic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/baseten/Llama-3.2-3B-Instruct-pythonic
- SGLang
How to use baseten/Llama-3.2-3B-Instruct-pythonic 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 "baseten/Llama-3.2-3B-Instruct-pythonic" \ --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": "baseten/Llama-3.2-3B-Instruct-pythonic", "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 "baseten/Llama-3.2-3B-Instruct-pythonic" \ --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": "baseten/Llama-3.2-3B-Instruct-pythonic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use baseten/Llama-3.2-3B-Instruct-pythonic 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 baseten/Llama-3.2-3B-Instruct-pythonic 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 baseten/Llama-3.2-3B-Instruct-pythonic to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for baseten/Llama-3.2-3B-Instruct-pythonic to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="baseten/Llama-3.2-3B-Instruct-pythonic", max_seq_length=2048, ) - Docker Model Runner
How to use baseten/Llama-3.2-3B-Instruct-pythonic with Docker Model Runner:
docker model run hf.co/baseten/Llama-3.2-3B-Instruct-pythonic
| {{- bos_token }} | |
| {%- if custom_tools is defined %} | |
| {%- set tools = custom_tools %} | |
| {%- endif %} | |
| {%- if not tools_in_user_message is defined %} | |
| {%- set tools_in_user_message = false %} | |
| {%- endif %} | |
| {%- if not date_string is defined %} | |
| {%- if strftime_now is defined %} | |
| {%- set date_string = strftime_now("%d %b %Y") %} | |
| {%- else %} | |
| {%- set date_string = "26 Jul 2024" %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if not tools is defined %} | |
| {%- set tools = none %} | |
| {%- endif %} | |
| {#- This block extracts the system message, so we can slot it into the right place. #} | |
| {%- if messages[0]['role'] == 'system' %} | |
| {%- set system_message = messages[0]['content']|trim %} | |
| {%- set messages = messages[1:] %} | |
| {%- else %} | |
| {%- set system_message = "You are a helpful assistant with tool calling capabilities. Only reply with a tool call if the function exists in the library provided by the user. If it doesn't exist, just reply directly in natural language. When you receive a tool call response, use the output to format an answer to the original user question." %} | |
| {%- endif %} | |
| {#- System message #} | |
| {{- "<|start_header_id|>system<|end_header_id|>\n\n" }} | |
| {%- if tools is not none %} | |
| {{- "Environment: ipython\n" }} | |
| {%- endif %} | |
| {{- "Cutting Knowledge Date: January 2024\n" }} | |
| {{- "Today Date: " + date_string + "\n\n" }} | |
| {%- if tools is not none and not tools_in_user_message %} | |
| {{- "You have access to the following functions. To call functions, please respond with a python list of the calls. " }} | |
| {{- 'Respond in the format [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] ' }} | |
| {{- "Do not use variables.\n\n" }} | |
| {%- for t in tools %} | |
| {{- t | tojson(indent=4) }} | |
| {{- "\n\n" }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- system_message }} | |
| {{- "<|eot_id|>" }} | |
| {#- Custom tools are passed in a user message with some extra guidance #} | |
| {%- if tools_in_user_message and not tools is none %} | |
| {#- Extract the first user message so we can plug it in here #} | |
| {%- if messages | length != 0 %} | |
| {%- set first_user_message = messages[0]['content']|trim %} | |
| {%- set messages = messages[1:] %} | |
| {%- else %} | |
| {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} | |
| {%- endif %} | |
| {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} | |
| {{- "Given the following functions, please respond with a python list for function calls " }} | |
| {{- "with their proper arguments to best answer the given prompt.\n\n" }} | |
| {{- 'Respond in the format [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] ' }} | |
| {{- "Do not use variables.\n\n" }} | |
| {%- for t in tools %} | |
| {{- t | tojson(indent=4) }} | |
| {{- "\n\n" }} | |
| {%- endfor %} | |
| {{- first_user_message + "<|eot_id|>"}} | |
| {%- endif %} | |
| {%- for message in messages %} | |
| {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} | |
| {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} | |
| {%- elif 'tool_calls' in message %} | |
| {{- '<|start_header_id|>assistant<|end_header_id|>\n\n[' -}} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- tool_call.name + '(' -}} | |
| {%- if tool_call.arguments is string %} | |
| {{- tool_call.arguments[2:-2] | replace('\\', '') | replace('\"', '') -}} | |
| {%- else %} | |
| {%- for param in tool_call.arguments %} | |
| {{- param + '=' -}} | |
| {{- "%s" | format(tool_call.arguments[param]) -}} | |
| {% if not loop.last %}, {% endif %} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- ')' -}} | |
| {% if not loop.last %}, {% endif %} | |
| {%- endfor %} | |
| {{- ']<|eot_id|>' -}} | |
| {%- elif message.role == "tool" or message.role == "ipython" %} | |
| {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} | |
| {%- if message.content is mapping %} | |
| {{- message.content | tojson }} | |
| {%- else %} | |
| {{- { "output": message.content } | tojson }} | |
| {%- endif %} | |
| {{- "<|eot_id|>" }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} | |
| {%- endif %} |