Instructions to use SVELA-task/model1b_task2_klmin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SVELA-task/model1b_task2_klmin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SVELA-task/model1b_task2_klmin") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SVELA-task/model1b_task2_klmin") model = AutoModelForCausalLM.from_pretrained("SVELA-task/model1b_task2_klmin") 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
- vLLM
How to use SVELA-task/model1b_task2_klmin with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SVELA-task/model1b_task2_klmin" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SVELA-task/model1b_task2_klmin", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SVELA-task/model1b_task2_klmin
- SGLang
How to use SVELA-task/model1b_task2_klmin 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 "SVELA-task/model1b_task2_klmin" \ --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": "SVELA-task/model1b_task2_klmin", "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 "SVELA-task/model1b_task2_klmin" \ --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": "SVELA-task/model1b_task2_klmin", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SVELA-task/model1b_task2_klmin with Docker Model Runner:
docker model run hf.co/SVELA-task/model1b_task2_klmin
| {{- 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 = true %} | |
| {%- 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 = "" %} | |
| {%- endif %} | |
| {#- System message #} | |
| {{- "<|start_header_id|>system<|end_header_id|>\n\n" }} | |
| {%- if tools is not none %} | |
| {{- "Environment: ipython\n" }} | |
| {%- endif %} | |
| {{- "Cutting Knowledge Date: December 2023\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 a function, please respond with JSON for a function call." }} | |
| {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} | |
| {{- "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 JSON for a function call " }} | |
| {{- "with its proper arguments that best answers the given prompt.\n\n" }} | |
| {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} | |
| {{- "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 %} | |
| {%- if not message.tool_calls|length == 1 %} | |
| {{- raise_exception("This model only supports single tool-calls at once!") }} | |
| {%- endif %} | |
| {%- set tool_call = message.tool_calls[0].function %} | |
| {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} | |
| {{- '{"name": "' + tool_call.name + '", ' }} | |
| {{- '"parameters": ' }} | |
| {{- tool_call.arguments | tojson }} | |
| {{- "}" }} | |
| {{- "<|eot_id|>" }} | |
| {%- elif message.role == "tool" or message.role == "ipython" %} | |
| {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} | |
| {%- if message.content is mapping or message.content is iterable %} | |
| {{- message.content | tojson }} | |
| {%- else %} | |
| {{- message.content }} | |
| {%- endif %} | |
| {{- "<|eot_id|>" }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} | |
| {%- endif %} | |