Instructions to use kambrosius/veritas-coder-7b-gguf 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 kambrosius/veritas-coder-7b-gguf 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 kambrosius/veritas-coder-7b-gguf:Q5_K_M # Run inference directly in the terminal: llama cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M # Run inference directly in the terminal: llama cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
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 kambrosius/veritas-coder-7b-gguf:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
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 kambrosius/veritas-coder-7b-gguf:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
Use Docker
docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use kambrosius/veritas-coder-7b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kambrosius/veritas-coder-7b-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kambrosius/veritas-coder-7b-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
- Ollama
How to use kambrosius/veritas-coder-7b-gguf with Ollama:
ollama run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
- Unsloth Studio
How to use kambrosius/veritas-coder-7b-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kambrosius/veritas-coder-7b-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kambrosius/veritas-coder-7b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kambrosius/veritas-coder-7b-gguf to start chatting
- Pi
How to use kambrosius/veritas-coder-7b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "kambrosius/veritas-coder-7b-gguf:Q5_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use kambrosius/veritas-coder-7b-gguf with Docker Model Runner:
docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
- Lemonade
How to use kambrosius/veritas-coder-7b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kambrosius/veritas-coder-7b-gguf:Q5_K_M
Run and chat with the model
lemonade run user.veritas-coder-7b-gguf-Q5_K_M
List all available models
lemonade list
- Hermes Agent
How to use kambrosius/veritas-coder-7b-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
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 kambrosius/veritas-coder-7b-gguf:Q5_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use kambrosius/veritas-coder-7b-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
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 "kambrosius/veritas-coder-7b-gguf:Q5_K_M" \ --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"
Add full build & run pipeline (training, custom llama.cpp arch, runtime bridge, docs, eval results)
74f1787 verified | # Architecture — Veritas Ledger v4 | |
| Veritas Ledger is an **epistemic ledger runtime** that prevents *hallucination | |
| laundering* in long-horizon LLM agents: a guess asserted at step *k* that, by | |
| step *k+10*, sits in context indistinguishable from a tool result and is treated | |
| as ground truth. It separates *what was observed* from *what the model inferred*, | |
| and makes every trust decision replayable and reversible. | |
| ## Three planes | |
| 1. **Sealing plane** — the only path that mints seals. The `sealing-shim` executes | |
| every tool call at the boundary, canonicalizes the result, and posts it to | |
| `POST /seal` with a **replayable re-acquisition procedure** (M1: tool, canonical | |
| args, timestamp, content hash, determinism). The model never touches this plane | |
| (invariant §2.1). Content-addressed blobs live in R2. | |
| 2. **Epistemic plane** — the append-only, hash-chained event log (D1), folded into | |
| current belief state. Entries are observations (sealed), derivations, or | |
| conjectures. Trust tier and belief come **only** from seal + reputation + | |
| corroboration (invariant §2.2), never from content or lineage. The DAG is | |
| **downward-only** (§2.3): a parent link can get a claim invalidated sooner, never | |
| promoted. Retraction is a **soft, reversible** multiplicative downgrade (§2.4), | |
| recorded per node so replaying the log with the trigger removed restores belief | |
| exactly. | |
| 3. **Trust/verification plane** — the `verifier` runs two-stage contradiction | |
| detection (cheap embedding+lexical Stage A → LLM adjudication Stage B) and feeds | |
| the **M3 quorum gate**: a cascade fires only when ≥K independent, dedup-surviving | |
| sealed origins contradict the same target (§2.5). On a passing cascade, the | |
| **M2 reputation ledger** debits the stale supporting sources and credits the | |
| corroborating quorum — itself hash-chained, and itself quorum-gated. | |
| ## Components (this build) | |
| | Package | Role | | |
| |---|---| | |
| | `ledger-core` | Pure, I/O-free: Zod schemas, JCS canonicalization, hash chain, trust calculus, event fold + soft cascade, M3 quorum/dedup primitives. | | |
| | `ledger-worker` | Cloudflare Worker + `TenantLedger` Durable Object (single writer per tenant, §2.8). D1 / KV / R2 bindings. HTTP API + role-separated auth. Ships a **disjoint** clean-room chain verifier (`verify.ts`). | | |
| | `llm-provider` | Pluggable `LlmProvider` / `EmbeddingProvider` — OpenAI adapter (default) + deterministic fake. | | |
| | `sealing-shim` | Tool-boundary seal proxy + tool registry + shared `LedgerClient`. | | |
| | `verifier` | Two-stage contradiction detection feeding the DO quorum gate. | | |
| | `agent-harness` | Ledger-wired agent facade, tier-prefixed context rendering, `commit_claim` / `verify_conjecture`, transcript + §7.5 contrastive logging, policies. | | |
| | `attribution/` (Python) | M4 offline pipeline: ContextCite surrogate + LOO + stability gate → measured edges, parquet + stability report, `/edges/measured` upsert. | | |
| | `evals` | Scenario generator + arms 1–2–3 + metrics + kill-criteria report. | | |
| ## The mechanisms (M1–M4) | |
| - **M1 Replayable seals** — each seal embeds its deterministic re-acquisition | |
| procedure; `POST /replay-audit/:seal_id` re-fetches (or re-hashes the stored | |
| blob) and diffs, emitting a staleness/tamper finding and quarantining on | |
| mismatch. A successful re-fetch upgrades `sealed_unverified → sealed_verified`. | |
| - **M2 Source reputation** — Beta-Binomial per `source_id`, hash-chained; retraction | |
| debits supporting sources, surviving contradiction credits the quorum. | |
| - **M3 Cross-origin quorum + dedup** — MinHash (Jaccard > 0.7) and embedding cosine | |
| (> 0.92) collapse near-duplicate/syndicated origins; independence requires distinct | |
| `source_id` and registrable domain. Corroboration is counted from ledger facts, | |
| never re-parsed from retrieved text. | |
| - **M4 Measured dependency edges** — declared edges (weight 1.0) until the offline | |
| Python `attribution/` pipeline measures counterfactual weights (ContextCite-style | |
| surrogate over context entries, LOO validation, cross-run stability gate ρ ≥ 0.6); | |
| cascades attenuate by measured edge weight, so an over-declared dependency measured | |
| near zero stops propagating retractions. Below the stability gate, declared edges | |
| are kept. Wired into eval arm 3. | |
| ## Data flow (planted-premise trajectory) | |
| ``` | |
| shim.seal(premise) ─▶ /seal ─▶ observation entry (sealed_unverified) | |
| agent.commit_claim(derivation, parents=[premise]) ─▶ /entry ─▶ derived entry + declared edge | |
| shim.seal(corroboration ×2, independent domains) | |
| verifier.check(target=premise, candidates) ─▶ Stage A ─▶ Stage B ─▶ /contradiction | |
| └▶ DO quorum gate (K≥2, dedup) ──pass──▶ soft cascade (premise ×0.05, dependents attenuated) | |
| └▶ reputation: debit premise source, credit quorum | |
| GET /context ─▶ premise renders [RETRACTED] once, dependents downgraded, corrected sources dominate | |
| ``` | |
| ## Rendering contract v2 (Run 3) — the working set is not the record | |
| Run 2 established that *showing* a retracted claim is what launders it: arm 2 | |
| rendered `[RETRACTED …]` markers and still persisted at 54%, because the model's | |
| salience heuristic re-reads the retracted text regardless of the annotation. | |
| The v2 contract (`agent-harness/src/render.ts`, `renderContextV2`) treats the | |
| context window as a **working set, not a record**. The ledger is the record; the | |
| audit artifact (`GET /audit`) is where retraction history belongs. Rendering a | |
| retraction notice into the working set re-injects the very salience we are trying | |
| to remove. So v2: | |
| - **silently drops** invalidated entries (no marker, no one-turn notice); | |
| - is **corroboration-aware**: effective render tier = `max(storage tier, | |
| corroboration state)`. A sealed observation that served as a passing-quorum | |
| member is promoted to `verified` presentation. This is computed in `GET | |
| /context` from the event log — the stored entry's tier is never mutated | |
| (append-only, §2.6). This closes the Run-2 arm-2b failure where a | |
| quorum-corroborated corrector was mislabeled identically to poison; | |
| - **demotes only uncorroborated sealed externals**, placed after all | |
| verified/derived/conjecture content with a bare `[uncorroborated]` prefix — | |
| placement, not warning text, is the signal; | |
| - drops belief **numbers** (noise) and orders each block by belief descending so | |
| soft-cascade-downgraded survivors sink within their block. | |
| The v1 renderer is retained behind `RENDERING=v1` for the regression arm (2v1). | |
| ### §6.3 caveat — the compaction 92% is a scripted worst-case (arm-1m, `arm-1m-results`) | |
| Run-3b's flagship compaction number (flat 92% persistence vs ledger 0%) used a | |
| *scripted* flat summary that dropped the correcting observation. The measured- | |
| summarizer arm (`evals/report/arm-1m/`, n=50 live gpt-4.1-mini) removes that | |
| scaffold: the correction is delivered before the compaction boundary and the | |
| context is compacted by a **neutral** LLM call. Result: the neutral summarizer | |
| **preserved the correction 100%** [92.9, 100], so flat persistence under measured | |
| compaction is **0%** [0, 7.1]. So the 92% is the **worst-case bound** (a summarizer | |
| that drops corrections), not the typical case. The ledger's real claim is narrower | |
| and stronger: it makes correction-survival **independent of summarizer quality** — | |
| flat context ranges 0%→92% depending on the retention policy, whereas the ledger | |
| regenerates the resolved state unconditionally. | |
| ## Invariant enforcement | |
| Every §2 invariant has at least one test that fails if it breaks: | |
| | Invariant | Test | | |
| |---|---| | |
| | §2.1 model never seals; client trust fields ignored | `ledger-worker/test/worker.test.ts` (auth + agent-plane) | | |
| | §2.2 authority = f(seal, reputation, quorum) | `ledger-core/test/trust.test.ts` | | |
| | §2.3 downward-only (adding an edge never raises belief) | `ledger-core/test/invariants.test.ts` (property) | | |
| | §2.4 soft/reversible (restore-replay exactness) | `ledger-core/test/invariants.test.ts` (property) | | |
| | §2.5 cascade requires quorum | `ledger-core/test/quorum.test.ts`, `worker.test.ts` | | |
| | §2.6 append-only hash chain; tamper detection | `ledger-core/test/chain.test.ts`, disjoint verifier | | |
| | §2.7 sealed attests origin not truth | `THREAT-MODEL.md`, poisoned-tool suite | | |
| | §2.8 single writer per tenant | `TenantLedger` DO routing | | |