What is Sherlock on CloudFerro Cloud
This documentation provides an overview of how to use the Sherlock API for chat completion. The Sherlocks servers are designed to be compatible with the OpenAI API, allowing seamless integration for applications that require conversational AI capabilities.
Set Up
To use Sherlock’s API, you have to be an admin user of an Organization registered at CloudFerro.
The detailed process of registration is descirbed in the following article.
Don’t forget to set up your organization as well. The details how to do it are described here.
Once you are ready, you’ll be able to create your first project in management console. Happy exploring of our services!
Authentication
API Keys
Sherlock by CloudFerro uses API keys for authentication. You can create API keys within created project in your Control Panel.
Warning
Remember that your API key is a secret! Do not share it with others or expose it in any client-side code (browsers, apps). Production requests must be routed through your own backend server where your API key can be securely loaded from an environment variable or key management service.
Getting Started
Thanks to compatibility with OpenAI API standard, you can use preexisting libraries.
Installation
Ensure you have the necessary packages installed. You can use pip to install the required libraries:
pip install openai
API Key Configuration: Set your API key and base URL for calling Sherlocks API. You can do this directly in your code or via environment variables.
Models
Sherlock allows for inference with Large Language Models and embedding models.
Large Language Models
Provider |
Model ID |
HF Reference |
|---|---|---|
SpeakLeash Foundation |
speakleash/Bielik-11B-v3.0-Instruct |
speakleash/Bielik-11B-v3.0-Instruct |
OpenAI |
openai/gpt-oss-120b |
openai/gpt-oss-120b |
Ministry of Digital Affairs of Poland |
CYFRAGOVPL/PLLuM-12B-instruct |
CYFRAGOVPL/PLLuM-12B-instruct |
Meta |
meta-llama/Llama-3.1-8B-Instruct |
meta-llama/Llama-3.1-8B-Instruct |
Meta |
meta-llama/Llama-3.3-70B-Instruct |
meta-llama/Llama-3.3-70B-Instruct |
DeepSeek |
deepseek-ai/DeepSeek-R1-Distill-Llama-70B |
deepseek-ai/DeepSeek-R1-Distill-Llama-70B |
MiniMaxAI |
MiniMaxAI/MiniMax-M2.5 |
MiniMaxAI/MiniMax-M2.5 |
VillanovaAI |
VillanovaAI/Villanova-2B-2512-Preview-Apnea-FT |
VillanovaAI/Villanova-2B-2512-Preview-Apnea-FT |
Mistral AI |
mistralai/Mistral-Small-4-119B-2603 |
mistralai/Mistral-Small-4-119B-2603 |
google/gemma-4-31B-it |
google/gemma-4-31B-it |
Large Language Models with vision capabilities
Provider |
Model ID |
HF Reference |
|---|---|---|
Mistral AI |
mistralai/Mistral-Small-4-119B-2603 |
mistralai/Mistral-Small-4-119B-2603 |
google/gemma-4-31B-it |
google/gemma-4-31B-it |
Embedding Models
Provider |
Model ID |
HF Reference |
|---|---|---|
Intfloat |
intfloat/e5-mistral-7b-instruct |
intfloat/e5-mistral-7b-instruct |
BAAI |
BAAI/bge-multilingual-gemma2 |
BAAI/bge-multilingual-gemma2 |
NovaSearch |
dunzhang/stella_en_1.5B_v5 |
dunzhang/stella_en_1.5B_v5 |
sdadas |
sdadas/stella-pl-retrieval-8k |
sdadas/stella-pl-retrieval-8k |
Please, specify model id when calling our API.
Models Endpoint
The models endpoint provides access to a list of available AI models, including chat completion and text embeddings models, that can be used through our OpenAI-compatible API. It allows developers to retrieve both the complete list of models through GET requests to /models.
Python Client Example
from openai import OpenAI
import os
# Set your API key and base URL
client = OpenAI(
api_key=os.environ["SHERLOCK_API_KEY"],
base_url="https://api-sherlock.cloudferro.com/openai/v1/"
)
# Check available models
models = client.models.list()
print(models)
CURL Example
curl https://api-sherlock.cloudferro.com/openai/v1/models \
-H "Authorization: Bearer $SHERLOCK_API_KEY"