Instructions to use prithivMLmods/Deepthink-Reasoning-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Deepthink-Reasoning-14B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Deepthink-Reasoning-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Deepthink-Reasoning-14B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Deepthink-Reasoning-14B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Deepthink-Reasoning-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Deepthink-Reasoning-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Deepthink-Reasoning-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Deepthink-Reasoning-14B

SGLang

How to use prithivMLmods/Deepthink-Reasoning-14B 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 "prithivMLmods/Deepthink-Reasoning-14B" \
    --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": "prithivMLmods/Deepthink-Reasoning-14B",
		"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 "prithivMLmods/Deepthink-Reasoning-14B" \
        --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": "prithivMLmods/Deepthink-Reasoning-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Deepthink-Reasoning-14B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Deepthink-Reasoning-14B
```

prithivMLmods commited on Jan 22, 2025

Commit

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@@ -15,4 +15,15 @@ tags:
 - Math
 - text-generation-inference
 - Math-CoT
----

 - Math
 - text-generation-inference
 - Math-CoT
+---
+# **Deepthink-Reasoning-14B**
+The *Deepthink-Reasoning-14B* model is a fine-tuned version of the *Qwen2.5* base model, designed for text generation tasks requiring deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.
+With its robust natural language processing capabilities, *Deepthink-Reasoning-14B* excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates an advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.
+- It possesses significantly **more knowledge** and exhibits greatly improved capabilities in **coding** and **mathematics**, thanks to specialized expert models in these domains.
+- Offers substantial improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g., tables), and **producing structured outputs**, especially in JSON format. It is **more resilient to diverse system prompts**, enhancing role-play implementation and condition-setting for chatbots.
+- Provides **long-context support** for up to 128K tokens and can generate up to 8K tokens.
+- Features **multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.