Instructions to use saishshinde15/Clyrai_Base_Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishshinde15/Clyrai_Base_Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="saishshinde15/Clyrai_Base_Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saishshinde15/Clyrai_Base_Reasoning")
model = AutoModelForCausalLM.from_pretrained("saishshinde15/Clyrai_Base_Reasoning")
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 saishshinde15/Clyrai_Base_Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saishshinde15/Clyrai_Base_Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishshinde15/Clyrai_Base_Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saishshinde15/Clyrai_Base_Reasoning

SGLang

How to use saishshinde15/Clyrai_Base_Reasoning 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 "saishshinde15/Clyrai_Base_Reasoning" \
    --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": "saishshinde15/Clyrai_Base_Reasoning",
		"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 "saishshinde15/Clyrai_Base_Reasoning" \
        --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": "saishshinde15/Clyrai_Base_Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saishshinde15/Clyrai_Base_Reasoning with Docker Model Runner:
```
docker model run hf.co/saishshinde15/Clyrai_Base_Reasoning
```

saishshinde15 commited on Aug 6, 2025

Commit

7204b98

verified ·

1 Parent(s): f4d0e5f

Update README.md

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@@ -24,7 +24,7 @@ language:
 - ara
 ---
-# Clyrai Secure Reasoning Model
 - **Developed by:** Clyrai
 - **License:** apache-2.0
@@ -35,7 +35,7 @@ language:
 ## **Model Description**
 Clyrai Secure Reasoning Model is a cutting-edge AI model designed for secure, reliable, and structured reasoning. Fine-tuned on Qwen 2.5 using GRPO, it enhances logical reasoning, decision-making, and problem-solving capabilities while maintaining a strong focus on reducing AI hallucinations and ensuring factual accuracy.
-Unlike conventional language models that rely primarily on knowledge retrieval, TBH.AI's model is designed to autonomously engage with complex problems, breaking them down into structured thought processes. Inspired by DeepSeek-R1, it employs advanced reinforcement learning methodologies that allow it to validate and refine its logical conclusions securely and effectively.
 This model is particularly suited for tasks requiring high-level reasoning, structured analysis, and problem-solving in critical domains such as cybersecurity, finance, and research. It is ideal for professionals and organizations seeking AI solutions that prioritize security, transparency, and truthfulness.

 - ara
 ---
+# Clyrai Secure Reasoning Model (Formerly known as TBH.AI_Base_Reasoning)
 - **Developed by:** Clyrai
 - **License:** apache-2.0
 ## **Model Description**
 Clyrai Secure Reasoning Model is a cutting-edge AI model designed for secure, reliable, and structured reasoning. Fine-tuned on Qwen 2.5 using GRPO, it enhances logical reasoning, decision-making, and problem-solving capabilities while maintaining a strong focus on reducing AI hallucinations and ensuring factual accuracy.
+Unlike conventional language models that rely primarily on knowledge retrieval, Clyrai's model is designed to autonomously engage with complex problems, breaking them down into structured thought processes. Inspired by DeepSeek-R1, it employs advanced reinforcement learning methodologies that allow it to validate and refine its logical conclusions securely and effectively.
 This model is particularly suited for tasks requiring high-level reasoning, structured analysis, and problem-solving in critical domains such as cybersecurity, finance, and research. It is ideal for professionals and organizations seeking AI solutions that prioritize security, transparency, and truthfulness.