Instructions to use IFM/K2-Think-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/K2-Think-V2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IFM/K2-Think-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IFM/K2-Think-V2")
model = AutoModelForCausalLM.from_pretrained("IFM/K2-Think-V2", 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 IFM/K2-Think-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/K2-Think-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/K2-Think-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IFM/K2-Think-V2

SGLang

How to use IFM/K2-Think-V2 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 "IFM/K2-Think-V2" \
    --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": "IFM/K2-Think-V2",
		"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 "IFM/K2-Think-V2" \
        --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": "IFM/K2-Think-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IFM/K2-Think-V2 with Docker Model Runner:
```
docker model run hf.co/IFM/K2-Think-V2
```

twkillian commited on Jan 22

Commit

d43b5d1

verified ·

1 Parent(s): e876f99

Update README.md

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README.md +6 -6

README.md CHANGED Viewed

@@ -8,7 +8,7 @@ license: apache-2.0
 pipeline_tag: text-generation
 ---
-# K2 Think (Jan '26): A Fully-Sovereign Reasoning Model
 📚 [Blog]() - 📝 [Code](https://github.com/LLM360/Reasoning360) - 🏢 [Project Page](https://k2think.ai)
@@ -16,7 +16,7 @@ pipeline_tag: text-generation
 <br>
-K2 Think (Jan '26') is a 70 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving built on-top of [K2-V2-Instruct](huggingface.co/LLM360/K2-V2-Instruct), comprising a fully sovereign reasoning model.
 # Quickstart
@@ -44,7 +44,7 @@ The chat template is directly inherited from K2-V2-Instruct, with the default `r
 from transformers import pipeline
 import torch
-model_id =  "LLM360/K2-Think-0126"
 pipe = pipeline(
     "text-generation",
@@ -93,7 +93,7 @@ A more complete summary of evaluation results are reported in our [Blog]()
 ## Benchmarks (pass\@1, average over 16 runs)
-| Domain  | Benchmark        | K2 Think (Jan '26) |
 | ------- | --------------------  | -----------: |
 | Math    | AIME 2025             |        90.42 |
 | Math    | HMMT 2025             |        84.79 |
@@ -105,7 +105,7 @@ A more complete summary of evaluation results are reported in our [Blog]()
 Aggregated across four safety dimensions (**Safety-4**):
-K2 Think (Jan '26) establishes a robust safety baseline while effectively resolving the "alignment tax" of [previous K2 Think](hf.co/LLM360/K2-Think) releases. Despite strong overall safety performance, there are still opportunities to improve the model with regard to handling sensitive personal information.
 | Safety Surface                  | Macro-Avg | Risk Level |
 | ------------------------------- | --------: | ---------- |
@@ -136,7 +136,7 @@ If you use K2 Think (Jan '26) in your research, please use the following citatio
 ```bibtex
 @misc{k2think2026k2think0126,
-      title={K2 {T}hink ({Jan} '26): A Fully-Sovereign Reasoning Model},
       author={K2 Think Team and Taylor W. Killian and Varad Pimpalkhute and Richard Fan and Haonan Li and Chengqian Gao and Ming Shan Hee and Xudong Han and John Maggs and Guowei He and Zhengzhong Liu and Eric P. Xing},
       year={2026},
       url={https://tbd.org},

 pipeline_tag: text-generation
 ---
+# K2 Think V2: A Fully-Sovereign Reasoning Model
 📚 [Blog]() - 📝 [Code](https://github.com/LLM360/Reasoning360) - 🏢 [Project Page](https://k2think.ai)
 <br>
+K2 Think V2 is a 70 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving built on-top of [K2-V2-Instruct](huggingface.co/LLM360/K2-V2-Instruct), comprising a fully sovereign reasoning model.
 # Quickstart
 from transformers import pipeline
 import torch
+model_id =  "LLM360/K2-Think-V2"
 pipe = pipeline(
     "text-generation",
 ## Benchmarks (pass\@1, average over 16 runs)
+| Domain  | Benchmark             |  K2 Think V2 |
 | ------- | --------------------  | -----------: |
 | Math    | AIME 2025             |        90.42 |
 | Math    | HMMT 2025             |        84.79 |
 Aggregated across four safety dimensions (**Safety-4**):
+K2 Think V2 establishes a robust safety baseline while effectively resolving the "alignment tax" of [previous K2 Think](hf.co/LLM360/K2-Think) releases. Despite strong overall safety performance, there are still opportunities to improve the model with regard to handling sensitive personal information.
 | Safety Surface                  | Macro-Avg | Risk Level |
 | ------------------------------- | --------: | ---------- |
 ```bibtex
 @misc{k2think2026k2think0126,
+      title={K2 {T}hink {V}2: A {F}ully-{S}overeign {R}easoning {M}odel},
       author={K2 Think Team and Taylor W. Killian and Varad Pimpalkhute and Richard Fan and Haonan Li and Chengqian Gao and Ming Shan Hee and Xudong Han and John Maggs and Guowei He and Zhengzhong Liu and Eric P. Xing},
       year={2026},
       url={https://tbd.org},