Text Generation
Transformers
Safetensors
deepseek_v3
conversational
custom_code
text-generation-inference
Instructions to use NousResearch/k2-merged-3.5T-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/k2-merged-3.5T-bf16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/k2-merged-3.5T-bf16", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/k2-merged-3.5T-bf16", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("NousResearch/k2-merged-3.5T-bf16", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NousResearch/k2-merged-3.5T-bf16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/k2-merged-3.5T-bf16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/k2-merged-3.5T-bf16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NousResearch/k2-merged-3.5T-bf16
- SGLang
How to use NousResearch/k2-merged-3.5T-bf16 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 "NousResearch/k2-merged-3.5T-bf16" \ --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": "NousResearch/k2-merged-3.5T-bf16", "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 "NousResearch/k2-merged-3.5T-bf16" \ --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": "NousResearch/k2-merged-3.5T-bf16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NousResearch/k2-merged-3.5T-bf16 with Docker Model Runner:
docker model run hf.co/NousResearch/k2-merged-3.5T-bf16
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50f807e | 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 | {%- if tools -%}
<|im_system|>tool_declare<|im_middle|>
# Tools
{{ tools | tojson }}<|im_end|>
{%- endif -%}
{%- for message in messages -%}
{%- if loop.first and messages[0]['role'] != 'system' -%}
<|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|>
{%- endif -%}
{%- set role_name = message.get('name') or message['role'] -%}
{%- if message['role'] == 'user' -%}
<|im_user|>{{role_name}}<|im_middle|>
{%- elif message['role'] == 'assistant' -%}
<|im_assistant|>{{role_name}}<|im_middle|>
{%- else -%}
<|im_system|>{{role_name}}<|im_middle|>
{%- endif -%}
{%- if message['role'] == 'assistant' and message.get('tool_calls') -%}
{%- if message['content'] -%}{{ message['content'] }}{%- endif -%}
<|tool_calls_section_begin|>
{%- for tool_call in message['tool_calls'] -%}
{%- set formatted_id = tool_call['id'] -%}
<|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
{%- endfor -%}
<|tool_calls_section_end|>
{%- elif message['role'] == 'tool' -%}
## Return of {{ message.tool_call_id }}
{{ message['content'] }}
{%- elif message['content'] is string -%}
{{ message['content'] }}
{%- elif message['content'] is not none -%}
{% for content in message['content'] -%}
{% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%}
<|media_start|>image<|media_content|><|media_pad|><|media_end|>
{% else -%}
{{ content['text'] }}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
<|im_end|>
{%- endfor -%}
{%- if add_generation_prompt -%}
<|im_assistant|>assistant<|im_middle|>
{%- endif -%} |