Text Generation
Transformers
Safetensors
English
jarvis_titan_moe
Mixture of Experts
deepseek-moe
titans-neural-memory
tri-brid-memory
differential-holographic-attention
dha-3
csa3-perturbative-attention
multi-token-prediction
reasoning
math
code
agentic
long-context
conversational
custom_code
Instructions to use dhanesh-hf/Jarvis-Titan-M4-Activated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhanesh-hf/Jarvis-Titan-M4-Activated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dhanesh-hf/Jarvis-Titan-M4-Activated", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("dhanesh-hf/Jarvis-Titan-M4-Activated", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dhanesh-hf/Jarvis-Titan-M4-Activated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dhanesh-hf/Jarvis-Titan-M4-Activated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dhanesh-hf/Jarvis-Titan-M4-Activated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dhanesh-hf/Jarvis-Titan-M4-Activated
- SGLang
How to use dhanesh-hf/Jarvis-Titan-M4-Activated 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 "dhanesh-hf/Jarvis-Titan-M4-Activated" \ --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": "dhanesh-hf/Jarvis-Titan-M4-Activated", "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 "dhanesh-hf/Jarvis-Titan-M4-Activated" \ --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": "dhanesh-hf/Jarvis-Titan-M4-Activated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dhanesh-hf/Jarvis-Titan-M4-Activated with Docker Model Runner:
docker model run hf.co/dhanesh-hf/Jarvis-Titan-M4-Activated
Download chat_template.jinja from dhanesh-hf/Jarvis-Titan-M4-Activated: direct link, hf CLI and curl.
- Browser
- Download file 3.14 kB
-
https://huggingface.co/dhanesh-hf/Jarvis-Titan-M4-Activated/resolve/main/chat_template.jinja
- Command line
-
hf download hf://dhanesh-hf/Jarvis-Titan-M4-Activated/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/dhanesh-hf/Jarvis-Titan-M4-Activated/resolve/main/chat_template.jinja
3.14 kB
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0]['role'] == 'system' %} | |
| {{- messages[0]['content'] }} | |
| {%- else %} | |
| {{- 'You are J.A.R.V.I.S., an expert autonomous intelligence agent engineered by Dhanesh. You have access to functions provided in <tools>...</tools> to execute actions deterministically and eliminate hallucinations. Always think before calling a tool inside <think>...</think> tags. When you receive a <tool_response>, verify the results before finalizing your response in <answer>...</answer>.' }} | |
| {%- endif %} | |
| {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user request.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} | |
| {%- for tool in tools %} | |
| {{- "\n" }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }} | |
| {%- else %} | |
| {%- if messages[0]['role'] == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} | |
| {%- else %} | |
| {{- '<|im_start|>system\nYou are J.A.R.V.I.S., an expert reasoning assistant engineered by Dhanesh. Before answering, think through the problem carefully inside <think>...</think> tags. Verify your reasoning. If you detect a flaw, correct it. Provide your final solution inside <answer>...</answer> tags. When the problem asks for a boxed answer, use \\boxed{your answer}. Be concise but complete.<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- for message in messages %} | |
| {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} | |
| {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == "assistant" %} | |
| {{- '<|im_start|>' + message.role }} | |
| {%- if message.content %} | |
| {{- '\n' + message.content }} | |
| {%- endif %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '\n<tool_call>\n{"name": "' }} | |
| {{- tool_call.name }} | |
| {{- '", "arguments": ' }} | |
| {{- tool_call.arguments | tojson }} | |
| {{- '}\n</tool_call>' }} | |
| {%- endfor %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} | |
| {{- '<|im_start|>user' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- message.content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- endif %} | |