Instructions to use opencerebral/Boris-2-Think-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opencerebral/Boris-2-Think-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="opencerebral/Boris-2-Think-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("opencerebral/Boris-2-Think-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use opencerebral/Boris-2-Think-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opencerebral/Boris-2-Think-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencerebral/Boris-2-Think-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/opencerebral/Boris-2-Think-Instruct

SGLang

How to use opencerebral/Boris-2-Think-Instruct 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 "opencerebral/Boris-2-Think-Instruct" \
    --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": "opencerebral/Boris-2-Think-Instruct",
		"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 "opencerebral/Boris-2-Think-Instruct" \
        --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": "opencerebral/Boris-2-Think-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use opencerebral/Boris-2-Think-Instruct with Docker Model Runner:
```
docker model run hf.co/opencerebral/Boris-2-Think-Instruct
```

Boris-2-Think-Instruct / boris_export.json

KlondikeDev

Release Boris-2-Think-Instruct

45c3b6a verified about 10 hours ago

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383 Bytes

{
  "name": "Boris-2-Think-Instruct",
  "step": "sft 4424",
  "tokens_seen": 200039464960,
  "source": "/media/joseph/Media/think-instruct/Boris-2-Think-Instruct.pt",
  "base_model": "opencerebral/Boris-2",
  "lineage": [
    "opencerebral/Boris-2 (200.04B tokens)",
    "+ full fine-tune on smol-smoltalk + 2x OpenThink hybrid (544,943 conversations), <think> format, 1 epoch"
  ]
}

	{
	"name": "Boris-2-Think-Instruct",
	"step": "sft 4424",
	"tokens_seen": 200039464960,
	"source": "/media/joseph/Media/think-instruct/Boris-2-Think-Instruct.pt",
	"base_model": "opencerebral/Boris-2",
	"lineage": [
	"opencerebral/Boris-2 (200.04B tokens)",
	"+ full fine-tune on smol-smoltalk + 2x OpenThink hybrid (544,943 conversations), <think> format, 1 epoch"
	]
	}