Text Generation
GGUF
llama.cpp
27b
jack
jack-xml
agentic
coding
coder
long-context
long-agentic
multi-turn
tool-calling
reasoning
16gb-vram
lm-studio
state-management
operative-recall
counterfactual-reasoning
deterministic-tools
global-workspace
imatrix
conversational
Instructions to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM # Run inference directly in the terminal: llama cli -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM # Run inference directly in the terminal: llama cli -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM # Run inference directly in the terminal: ./llama-cli -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM # Run inference directly in the terminal: ./build/bin/llama-cli -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Use Docker
docker model run hf.co/JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
- LM Studio
- Jan
- vLLM
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
- Ollama
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with Ollama:
ollama run hf.co/JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
- Unsloth Desktop
- Pi
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with Docker Model Runner:
docker model run hf.co/JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
- Lemonade
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Run and chat with the model
lemonade run user.Jack-3.8-27B-Coder-16GB-VRAM-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Restore thumbnail and update Jack 3.8 model card
Browse files
README.md
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tags:
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---
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# Jack-3.8-27B-Coder-16GB-VRAM
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```text
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LONG CONTEXT / NATIVE REASONING
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old state
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new state
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hypotheses
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more reasoning
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more reasoning
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more reasoning
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JACK XML REBINDING
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<workspace_state> operative state
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<grounded_source> evidence that controls the conclusion
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<anchor_fact> exact critical variables and identities
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<deterministic_check> verified external results when applicable
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<pitfall_check> active falsification / failure boundaries
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FINAL OUTPUT / ACTION
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plausible prior knowledge
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silently replaces
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the evidence actually supplied in context
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PLAN / NOVEL PROBLEM
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Native thinking: ON
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Jack XML: ACTIVE
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expansive planning, search, falsification
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ROUTINE EXECUTION
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Native thinking: OFF
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Jack XML: ACTIVE
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aligned execution under anchored state
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NEW FAILURE / UNCERTAINTY
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Native thinking: ON
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deep debugging and hypothesis search
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CONTINUED EXECUTION
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Native thinking: OFF
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Jack XML: ACTIVE
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efficient implementation
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INDEPENDENT REVIEW
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Native thinking: ON
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Jack XML: ACTIVE
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adversarial audit and recalibration
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```
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```text
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native thinking: OFF
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<workspace_state>
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<grounded_source>
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initial <anchor_fact> values
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<pitfall_check>
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detect inconsistency
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supersede incorrect anchors
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correct final authoritative state
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```
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```text
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requirement
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```
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Every earlier state may remain inside the context.
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```text
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original requirement
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derived plan
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implementation
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tests
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claimed verification
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```
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---
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# State-Preserving Plan
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Jack 3.8 has also been tested in workflows where native reasoning mode changes between stages.
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```text
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PLANNING
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Native thinking: ON
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-
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Jack reconstructs and anchors the authoritative plan
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EXECUTION
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Native thinking: OFF
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Jack carries out the established plan under active XML alignment
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REVIEW
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Native thinking: ON
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Jack independently falsifies the result
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```
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```text
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model hypothesis
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deterministic check
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verified result
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Jack rebinds verified result into operative state
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later reasoning must use, explain, or challenge the evidence explicitly
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```
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- planning precision
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- preservation of original requirements
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- malformed-input handling
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- test quality
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- independent review
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| Field | Value |
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|---|---|
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| Model | `Jack-3.8-27B-Coder-16GB-VRAM` |
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| Author | Jonathan Michael Langford
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| Contact | `mlangford75@protonmail.com` |
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| Project / Research | https://github.com/mlangford75-lgtm/mlangford75-lgtm |
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| GGUF | `Jack-3.8-27B-Coder-16GB-VRAM.gguf` |
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> **How much effective agentic intelligence can be recovered from local hardware by organizing cognition better rather than relying only on more parameters, more context, or permanently enabled deliberation?**
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That is the research program.
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---
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tags:
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- 27b
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- gguf
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---
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# Jack-3.8-27B-Coder-16GB-VRAM
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<p align="center">
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<img src="https://huggingface.co/JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM/resolve/main/jack-3.8-coder.png" alt="Jack 3.8 Coder" width="600">
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</p>
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> **27B coding intelligence. Structured cognitive control. One 16GB GPU.**
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```text
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LONG CONTEXT / NATIVE REASONING
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+
────────────────────────────────────────────
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old state
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new state
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hypotheses
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more reasoning
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more reasoning
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more reasoning
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+
│
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+
â–¼
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JACK XML REBINDING
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+
────────────────────────────────────────────
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<workspace_state> operative state
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<grounded_source> evidence that controls the conclusion
|
| 183 |
<anchor_fact> exact critical variables and identities
|
| 184 |
<deterministic_check> verified external results when applicable
|
| 185 |
<pitfall_check> active falsification / failure boundaries
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| 186 |
+
│
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+
â–¼
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FINAL OUTPUT / ACTION
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```
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```text
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plausible prior knowledge
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+
↓
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silently replaces
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+
↓
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the evidence actually supplied in context
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```
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PLAN / NOVEL PROBLEM
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Native thinking: ON
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Jack XML: ACTIVE
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+
│
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| 380 |
+
â–¼
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| 381 |
expansive planning, search, falsification
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| 382 |
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ROUTINE EXECUTION
|
| 384 |
Native thinking: OFF
|
| 385 |
Jack XML: ACTIVE
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| 386 |
+
│
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| 387 |
+
â–¼
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| 388 |
aligned execution under anchored state
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| 389 |
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NEW FAILURE / UNCERTAINTY
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| 391 |
Native thinking: ON
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| 392 |
Jack XML: ACTIVE
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| 393 |
+
│
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| 394 |
+
â–¼
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| 395 |
deep debugging and hypothesis search
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| 397 |
CONTINUED EXECUTION
|
| 398 |
Native thinking: OFF
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| 399 |
Jack XML: ACTIVE
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| 400 |
+
│
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| 401 |
+
â–¼
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efficient implementation
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| 403 |
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INDEPENDENT REVIEW
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| 405 |
Native thinking: ON
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| 406 |
Jack XML: ACTIVE
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| 407 |
+
│
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+
â–¼
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adversarial audit and recalibration
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```
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```text
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native thinking: OFF
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↓
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<workspace_state>
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+
↓
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<grounded_source>
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+
↓
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initial <anchor_fact> values
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| 452 |
+
↓
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<pitfall_check>
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| 454 |
+
↓
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| 455 |
detect inconsistency
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| 456 |
+
↓
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| 457 |
supersede incorrect anchors
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| 458 |
+
↓
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| 459 |
correct final authoritative state
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```
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```text
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requirement
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+
→ plan
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→ implementation
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→ failure
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→ diagnosis
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→ revised plan
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→ new artifact
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→ test
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→ external review
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+
→ correction
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→ final verification
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```
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Every earlier state may remain inside the context.
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| 549 |
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```text
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| 551 |
original requirement
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| 552 |
+
↕
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| 553 |
derived plan
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| 554 |
+
↕
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| 555 |
implementation
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| 556 |
+
↕
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| 557 |
tests
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+
↕
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| 559 |
claimed verification
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| 560 |
```
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| 576 |
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| 577 |
---
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+
# State-Preserving Plan → Execute → Review
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Jack 3.8 has also been tested in workflows where native reasoning mode changes between stages.
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| 582 |
|
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```text
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PLANNING
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| 587 |
Native thinking: ON
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| 588 |
+
↓
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| 589 |
Jack reconstructs and anchors the authoritative plan
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| 590 |
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| 591 |
EXECUTION
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| 592 |
Native thinking: OFF
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| 593 |
+
↓
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| 594 |
Jack carries out the established plan under active XML alignment
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| 595 |
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| 596 |
REVIEW
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| 597 |
Native thinking: ON
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| 598 |
+
↓
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Jack independently falsifies the result
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```
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```text
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model hypothesis
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+
↓
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deterministic check
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| 636 |
+
↓
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| 637 |
verified result
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| 638 |
+
↓
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| 639 |
Jack rebinds verified result into operative state
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+
↓
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later reasoning must use, explain, or challenge the evidence explicitly
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```
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- planning precision
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- preservation of original requirements
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+
- plan → execution fidelity
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- malformed-input handling
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- test quality
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- independent review
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| Field | Value |
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|---|---|
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| Model | `Jack-3.8-27B-Coder-16GB-VRAM` |
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+
| Author | Jonathan Michael Langford — Independent Researcher, Lead Architect, The Jack Project |
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| Contact | `mlangford75@protonmail.com` |
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| Project / Research | https://github.com/mlangford75-lgtm/mlangford75-lgtm |
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| GGUF | `Jack-3.8-27B-Coder-16GB-VRAM.gguf` |
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> **How much effective agentic intelligence can be recovered from local hardware by organizing cognition better rather than relying only on more parameters, more context, or permanently enabled deliberation?**
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That is the research program.
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+
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