Image-Text-to-Text
Transformers
Safetensors
deepseek_v41
text-generation
conversational
8-bit precision
fp8
Instructions to use maletsidibeOS/Anos_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maletsidibeOS/Anos_AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="maletsidibeOS/Anos_AI") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("maletsidibeOS/Anos_AI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use maletsidibeOS/Anos_AI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maletsidibeOS/Anos_AI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maletsidibeOS/Anos_AI", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/maletsidibeOS/Anos_AI
- SGLang
How to use maletsidibeOS/Anos_AI 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 "maletsidibeOS/Anos_AI" \ --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": "maletsidibeOS/Anos_AI", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "maletsidibeOS/Anos_AI" \ --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": "maletsidibeOS/Anos_AI", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use maletsidibeOS/Anos_AI with Docker Model Runner:
docker model run hf.co/maletsidibeOS/Anos_AI
Download encoding/tests/test_output_3.txt from maletsidibeOS/Anos_AI: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/maletsidibeOS/Anos_AI/resolve/main/encoding/tests/test_output_3.txt
- Command line
-
hf download hf://maletsidibeOS/Anos_AI/encoding/tests/test_output_3.txt
-
curl -L -o test_output_3.txt https://huggingface.co/maletsidibeOS/Anos_AI/resolve/main/encoding/tests/test_output_3.txt
2.48 kB
| <|begin▁of▁sentence|><|System|>该助手为DeepSeek,由深度求索公司创造。<|latest_reminder|>2026-02-21,星期六,广州,App,中文<|System|>CITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】 | |
| ## Tools | |
| You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<|DSML| calls>" block like the following: | |
| <|DSML| calls> | |
| <|DSML| invoke name="$TOOL_NAME"> | |
| <|DSML| parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</|DSML| parameter> | |
| ... | |
| </|DSML| invoke> | |
| <|DSML| invoke name="$TOOL_NAME2"> | |
| ... | |
| </|DSML| invoke> | |
| </|DSML| calls> | |
| String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`. | |
| If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response. | |
| Otherwise, output directly after </think> with tool calls or final response. | |
| ### Available Tool Schemas | |
| {"name": "search", "description": "Web search. Split multiple queries with '||'.", "parameters": {"type": "object", "properties": {"queries": {"type": "string", "description": "query1||query2"}}, "required": ["queries"], "additionalProperties": false}} | |
| {"name": "open", "description": "Batch open IDs (format 【{id}†...】) or URLs.", "parameters": {"type": "object", "properties": {"open_list": {"type": "array", "items": {"type": "object", "properties": {"id": {"description": "ID or URL", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "loc": {"type": "integer", "description": "Start line", "default": -1}, "num_lines": {"type": "integer", "description": "", "default": -1}}, "additionalProperties": false}, "description": ""}}, "required": ["open_list"], "additionalProperties": false}} | |
| You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. | |
| <|User|>小柴胡冲剂和布洛芬能一起吃吗?<|Assistant|></think> | |
| <|DSML| calls> | |
| <|DSML| invoke name="search"> | |
| <|DSML| parameter name="queries" string="true">小柴胡冲剂 布洛芬 相互作用 一起吃</|DSML| parameter> | |
| </|DSML| invoke> | |
| </|DSML| calls><|end▁of▁sentence|><|User|><tool_result>[0]</tool_result><|Assistant|></think>请及时就医。<|end▁of▁sentence|> |