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
File size: 2,912 Bytes
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"is_encoder_decoder": false,
"id2label": {
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"vocab_size": 152064,
"hidden_size": 3584,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
"hidden_act": "silu",
"max_position_embeddings": 32768,
"initializer_range": 0.02,
"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
"rope_parameters": {
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"model_type": "jarvis_titan_moe",
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"num_experts_per_tok": 2,
"expert_intermediate_size": 4736,
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"auto_map": {
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"AutoModelForCausalLM": "modeling_jarvis_titan_moe.JarvisTitanMoEForCausalLM"
},
"m4_calibrated": true,
"architecture_milestone": "M4",
"injection_layers": [
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"memory_dim": 512,
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"tribrid_target_distribution": [
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],
"upstream_m4_adapter": "dhanesh-hf/Jarvis-Titan-M4-Calibrated-Adapter",
"base_backbone": "dhanesh-hf/Jarvis-Titan-V14-MoE-Merged",
"m4_architecture": "tribrid_dha3_mtp",
"dha3_holographic_decoupling": true,
"dha3_anchor_interval": 4,
"csa3_delta_caching": true,
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"dynamic_mag_gating": true,
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"license": "other",
"license_name": "jtrl-v1.0"
} |