Instructions to use Theros/Q2.5-ColdBrew-R1-Obsidian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Theros/Q2.5-ColdBrew-R1-Obsidian with Transformers:

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

pipe = pipeline("text-generation", model="Theros/Q2.5-ColdBrew-R1-Obsidian")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Theros/Q2.5-ColdBrew-R1-Obsidian")
model = AutoModelForCausalLM.from_pretrained("Theros/Q2.5-ColdBrew-R1-Obsidian")
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 Theros/Q2.5-ColdBrew-R1-Obsidian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Theros/Q2.5-ColdBrew-R1-Obsidian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Theros/Q2.5-ColdBrew-R1-Obsidian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Theros/Q2.5-ColdBrew-R1-Obsidian

SGLang

How to use Theros/Q2.5-ColdBrew-R1-Obsidian 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 "Theros/Q2.5-ColdBrew-R1-Obsidian" \
    --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": "Theros/Q2.5-ColdBrew-R1-Obsidian",
		"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 "Theros/Q2.5-ColdBrew-R1-Obsidian" \
        --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": "Theros/Q2.5-ColdBrew-R1-Obsidian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Theros/Q2.5-ColdBrew-R1-Obsidian with Docker Model Runner:
```
docker model run hf.co/Theros/Q2.5-ColdBrew-R1-Obsidian
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Passthrough merge method.

Models Merged

The following models were included in the merge:

Theros/Qwen2.5-ColdBrew-R1

Configuration

The following YAML configuration was used to produce this model:

name: Q2.5-ColdBrew-R1-Obsidian
const_tag: &scale_factor 0.7071067812  # 1/sqrt(2) scaling for stability

attenuate-env: &attenuated_env
  parameters:
    scale:
      - filter: q_proj
        value: *scale_factor
      - filter: k_proj
        value: *scale_factor
      - value: 1.0

slices:
  - sources:
      - model: Theros/Qwen2.5-ColdBrew-R1
        layer_range: [0, 8]  # Retaining foundational knowledge and language structure.

  - sources:
      - model: Theros/Qwen2.5-ColdBrew-R1
        layer_range: [9, 19]  # Full-strength mid-range layers.

  - sources:
      - model: Theros/Qwen2.5-ColdBrew-R1
        layer_range: [10, 18]  # Targeted reinforcement, slightly attenuated to avoid over-dominance.
        <<: *attenuated_env

  - sources:
      - model: Theros/Qwen2.5-ColdBrew-R1
        layer_range: [19, 28]  # Keeping higher-level abstract processing untouched for stability.

merge_method: passthrough
dtype: bfloat16
normalize: true
int8_mask: true

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Safetensors

Model size

9B params

Tensor type

BF16

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Merges

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