Text Generation
Transformers
Safetensors
GGUF
English
Chinese
spark2_5
llm
sparkx2_5
conversational
custom_code
Instructions to use mohommed/Lucet-CoderSense-4B-Omega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohommed/Lucet-CoderSense-4B-Omega with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mohommed/Lucet-CoderSense-4B-Omega", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mohommed/Lucet-CoderSense-4B-Omega", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mohommed/Lucet-CoderSense-4B-Omega with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mohommed/Lucet-CoderSense-4B-Omega" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mohommed/Lucet-CoderSense-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mohommed/Lucet-CoderSense-4B-Omega
- SGLang
How to use mohommed/Lucet-CoderSense-4B-Omega 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 "mohommed/Lucet-CoderSense-4B-Omega" \ --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": "mohommed/Lucet-CoderSense-4B-Omega", "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 "mohommed/Lucet-CoderSense-4B-Omega" \ --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": "mohommed/Lucet-CoderSense-4B-Omega", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mohommed/Lucet-CoderSense-4B-Omega with Docker Model Runner:
docker model run hf.co/mohommed/Lucet-CoderSense-4B-Omega
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +23 -27
- lucet_harness.py +3 -4
- mmproj-model-f16.gguf +3 -0
- mmproj-vision-audio.safetensors +2 -2
- model.safetensors +1 -1
- omni_config.json +4 -6
- systemone_module.safetensors +1 -1
.gitattributes
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lucet-spark-4b-uncensored-gsq-rco-q4_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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base_model:
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# 🏛️
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**Lucet-Coder-Omega** is post-trained from **`XHToken/Spark-X2.6-4B`** (1M native context window, 54.3% SWE Pro, 71.8% BFCL-V4) using four advanced mathematical and systems-level algorithms:
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2. **Failure-Cluster Active Data Mining & AST Counterfactual Bug Immunization:** Diagnoses failure modes (`FAIL_TRUNCATED_THINK`, `FAIL_ASSERTION_BOUNDARY`, `FAIL_MISSING_IMPORT`, `FAIL_EPISTEMIC_HALLUCINATION`) across algorithmic clusters and dynamically pops **unseen neighbor tasks** + surgical AST counterfactual bugs from a disjoint reserve pool—achieving **+14.0% on Held-Out MBPP `pass@1`** and **+20.1% faster token efficiency** with zero training data repetition.
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3. **QR-SVD Gavish-Donoho Spectral Denoising:** Strips stochastic gradient noise singular values from low-rank adapters in $O(r^2(d_{\text{out}} + d_{\text{in}}))$ before merging into base weights.
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4. **Q4_K 32-Block Crest-Factor Equalization & KL-Bounded Uncensored-GSQ-v2:** Equalizes 32-element GGUF super-block crest factors ($\le 3.8\,\text{rms}$) alongside Gram-Schmidt orthogonal refusal projection + `llama-imatrix` calibration.
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## 📊
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license: apache-2.0
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library_name: transformers
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pipeline_tag: image-text-to-text
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base_model:
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tags:
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- spark
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- codersense
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- hybrid-attention
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- vision
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- audio
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- 7-senses
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---
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# 🏛️ Lucet-CoderSense-4B-Omega (7-Sense + SystemOne + J.A.R.V.I.S. Edition)
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**Lucet-CoderSense-4B-Omega** is a 4.1B 7-Sense Hybrid SWA/Global-Attention (27 SWA + 9 GA layers, 1M context) Reasoning, Coding, Blender 3D MCP, HTML5 Game Studio & Real-Time J.A.R.V.I.S. Agent built by **Mohommed**.
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## 📊 Verification Telemetry
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- **Held-Out MBPP pass@1**: `66.7%` -> `66.7%` (81 avg tokens)
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- **Epistemic Anti-Hallucination**: `12.5%` -> `75.0%`
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- **AIME Symbolic Math**: `50.0%` -> `50.0%`
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- **V8 HTML5 Game Studio**: `VERIFIED`
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- **Blender 3D Cartoon Animator**: `PASSED`
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- **7-Sense Multimodal Projector (`mmproj-model-f16.gguf`)**: Included for LM Studio & `llama.cpp` Vision support.
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lucet_harness.py
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"""
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Lucet-Omega 7-Sense Sovereign Harness & J.A.R.V.I.S. SDK (v6.
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Senses: Sight (Vision) + Hearing (Audio) + Proprioception (3D Z-Up) + Tactile (Collision) + Interoception (CPU/RAM) + Chronoception (24fps) + Symbolic Language
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Optimized for Intel Core i5-11400 (AVX-512, 6 Physical Cores) + 32GB DDR4 RAM
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"""
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import json
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import os
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mode, max_toks = self.route_system_mode(user_input, visual_surprise)
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vitals = self.read_interoception_vitals()
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sys_msg = (
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f"You are J.A.R.V.I.S., operating in {mode} mode with 7-Sense telemetry {vitals}. "
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"
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payload = json.dumps({
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"messages": [{"role": "system", "content": sys_msg}, {"role": "user", "content": user_input}],
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"""
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Lucet-CoderSense-4B-Omega 7-Sense Sovereign Harness & J.A.R.V.I.S. SDK (v6.2)
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Senses: Sight (Vision) + Hearing (Audio) + Proprioception (3D Z-Up) + Tactile (Collision) + Interoception (CPU/RAM) + Chronoception (24fps) + Symbolic Language
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"""
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import json
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import os
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mode, max_toks = self.route_system_mode(user_input, visual_surprise)
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vitals = self.read_interoception_vitals()
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sys_msg = (
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f"You are Lucet-CoderSense-4B-Omega (J.A.R.V.I.S.), operating in {mode} mode with 7-Sense telemetry {vitals}. "
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"Always wrap reasoning in <think>...\n</think>."
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)
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payload = json.dumps({
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"messages": [{"role": "system", "content": sys_msg}, {"role": "user", "content": user_input}],
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mmproj-model-f16.gguf
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mmproj-vision-audio.safetensors
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model.safetensors
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omni_config.json
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{
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"senses_active": [
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"2_Hearing_Whisper_60tok",
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"6_Chronoception_24fps_Timeline_4tok",
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"total_sensory_tokens": 133,
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"projector_file": "mmproj-vision-audio.safetensors",
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"systemone_module": "systemone_module.safetensors",
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"sovereign_harness_sdk": "lucet_harness.py",
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"systemone_tap_layer": 3,
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"llm_hidden_size": 2560,
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"swa_window_size": 512
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}
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{
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"model_name": "Lucet-CoderSense-4B-Omega",
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"senses_active": [
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"1_Sight_CLIP_ViT_50tok",
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"2_Hearing_Whisper_60tok",
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"3_Proprioception_3D_ZUp_8tok",
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"4_Tactile_Physics_Collision_8tok",
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"6_Chronoception_24fps_Timeline_4tok",
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"7_Symbolic_Language_Code_MCP"
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],
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"gguf_mmproj_file": "mmproj-model-f16.gguf",
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"safetensors_mmproj_file": "mmproj-vision-audio.safetensors",
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"systemone_module": "systemone_module.safetensors",
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"sovereign_harness_sdk": "lucet_harness.py",
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"llm_hidden_size": 2560,
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"swa_window_size": 512
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}
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systemone_module.safetensors
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size 13121770
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