Text Generation
Transformers
Safetensors
laguna
code
distillation
moe-to-dense
conversational
custom_code
Instructions to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="poolside-laguna-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True) 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("poolside-laguna-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True, 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "poolside-laguna-hackathon/laguna-xs2-dense-stage1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "poolside-laguna-hackathon/laguna-xs2-dense-stage1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage1
- SGLang
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 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 "poolside-laguna-hackathon/laguna-xs2-dense-stage1" \ --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": "poolside-laguna-hackathon/laguna-xs2-dense-stage1", "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 "poolside-laguna-hackathon/laguna-xs2-dense-stage1" \ --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": "poolside-laguna-hackathon/laguna-xs2-dense-stage1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use poolside-laguna-hackathon/laguna-xs2-dense-stage1 with Docker Model Runner:
docker model run hf.co/poolside-laguna-hackathon/laguna-xs2-dense-stage1
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d755f6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | {#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#}
{#- Adds a default system message (used when no system message is provided in `messages`). -#}
{{- "〈|EOS|〉" -}}
{%- set enable_thinking = enable_thinking | default(false) -%}
{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%}
{%- set add_generation_prompt = add_generation_prompt | default(false) -%}
{#- ───── header (system message) ───── -#}
{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%}
{%- if messages and messages[0].role == "system" -%}
{%- set system_message = messages[0].content -%}
{%- endif -%}
{%- if (system_message and system_message.strip()) or tools -%}
{{- "<system>\n" -}}
{%- if system_message and system_message.strip() -%}
{{- "\n" -}}
{{- system_message.rstrip() -}}
{%- endif -%}
{%- if tools -%}
{{- "\n\n### Tools\n\n" -}}
{%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n"
~ "All available function signatures are listed below:\n"
~ "<available_tools>\n") -%}
{%- for tool in tools -%}
{%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%}
{%- endfor -%}
{%- if enable_thinking -%}
{%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
"Wrap your thinking in '<think>', '</think>' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
"<think> your thoughts here </think>\n" ~
"<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
"</tool_call>" -%}
{%- else -%}
{%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
"For each function call, return an unescaped XML-like object " ~
"with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
"<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
"</tool_call>" -%}
{%- endif -%}
{{- tool_string -}}
{%- endif -%}
{{- "\n</system>\n" -}}
{%- endif -%}
{#- ───── main loop ───── -#}
{%- for message in messages -%}
{%- set content = message.content if message.content is string else "" -%}
{%- if message.role == "user" -%}
{{- "<user>\n" + content + "\n</user>\n" -}}
{%- elif message.role == "assistant" -%}
{%- generation -%}
{{- "<assistant>\n" -}}
{%- if render_assistant_messages_raw -%}
{#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#}
{#- The generation prompt is <think> when enable_thinking, </think> otherwise. -#}
{#- Only prepend if content doesn't already start with it. -#}
{%- if enable_thinking -%}
{%- if not content.startswith('<think>') -%}
{{- '<think>' -}}
{%- endif -%}
{%- else -%}
{%- if not content.startswith('</think>') -%}
{{- '</think>' -}}
{%- endif -%}
{%- endif -%}
{{- content -}}
{#- Append closing tag if content doesn't already end with it. -#}
{%- if not content.endswith('</assistant>\n') and not content.endswith('</assistant>') -%}
{{- '\n</assistant>' -}}
{%- endif -%}
{{- "\n" -}}
{%- else -%}
{#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from <think> tags -#}
{%- set reasoning_content = '' %}
{%- if message.reasoning is string %}
{%- set reasoning_content = message.reasoning %}
{%- elif message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- endif %}
{#- Always strip <think> tags from content if present to avoid duplication -#}
{%- if '</think>' in content %}
{%- if not reasoning_content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{#- Display reasoning content for all messages -#}
{%- if reasoning_content -%}
{{- '<think>\n' + reasoning_content.strip() + '\n</think>\n' -}}
{%- else -%}
{{- '</think>\n' -}}
{%- endif -%}
{#- Display main content -#}
{%- if content.strip() -%}
{{- content.strip() ~ "\n" -}}
{%- endif -%}
{%- if message.tool_calls -%}
{%- for tool_call in message.tool_calls -%}
{%- set function_data = tool_call.function -%}
{{- '<tool_call>' + function_data.name }}
{% set _args = function_data.arguments %}
{%- for k, v in _args.items() -%}
{{- "<arg_key>" ~ k ~ "</arg_key>\n" -}}
{{- "<arg_value>"}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "</arg_value>\n" -}}
{%- endfor -%}
{{- "</tool_call>\n" -}}
{%- endfor -%}
{%- endif -%}
{{- "</assistant>\n" -}}
{%- endif -%}
{%- endgeneration -%}
{%- elif message.role == "tool" -%}
{{- "<tool_response>\n" + content + "\n</tool_response>\n" -}}
{%- elif message.role == "system" and loop.index0 != 0 -%}
{#- Render additional system messages (skip the first one which is handled separately in the header) -#}
{{- "<system>\n" + content + "\n</system>\n" -}}
{%- endif -%}
{%- endfor -%}
{#- ───── generation prompt ───── -#}
{%- if add_generation_prompt -%}
{{- "<assistant>\n" -}}
{#- ───── Include reasoning mode directive ───── -#}
{%- if not enable_thinking %}
{{- '</think>' -}}
{%- else %}
{{- '<think>' -}}
{%- endif %}
{%- endif -%}
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