Instructions to use BlackwoodAI/LedgerGuard-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BlackwoodAI/LedgerGuard-35B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BlackwoodAI/LedgerGuard-35B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BlackwoodAI/LedgerGuard-35B") model = AutoModelForCausalLM.from_pretrained("BlackwoodAI/LedgerGuard-35B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BlackwoodAI/LedgerGuard-35B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BlackwoodAI/LedgerGuard-35B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlackwoodAI/LedgerGuard-35B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BlackwoodAI/LedgerGuard-35B
- SGLang
How to use BlackwoodAI/LedgerGuard-35B 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 "BlackwoodAI/LedgerGuard-35B" \ --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": "BlackwoodAI/LedgerGuard-35B", "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 "BlackwoodAI/LedgerGuard-35B" \ --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": "BlackwoodAI/LedgerGuard-35B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use BlackwoodAI/LedgerGuard-35B with Docker Model Runner:
docker model run hf.co/BlackwoodAI/LedgerGuard-35B
LedgerGuard-35B
LedgerGuard-35B is a full merged accounting workflow model built from
BlackwoodAI/LedgerGuard-35B revision 30a33754a1f4da76758e94637a92c9dd3c87ad2f with BlackwoodAI specialist
accounting behavior merged directly into the weights.
It is intended for personal, non-commercial research and evaluation only. It is not licensed for business use, hosted inference, SaaS, or model-as-a-service.
What It Is Built For
Accounting automation usually fails in the boring details: exact schema shape, source-linked decisions, conservative review routing, draft-only tool calls, and software-specific workflow discipline. LedgerGuard-35B is tuned for that specialist layer.
The model is designed for accounting-agent systems that need a disciplined proposal generator beside deterministic validators and a senior reviewer model.
Strengths
- Draft-only accounting workflow outputs
- Structured JSON for accounting review and workflow routing
- Source-linked decisions for reconciliations, bills, invoices, payouts, and close workflows
- Conservative handling of ambiguity, stale feeds, duplicate risk, missing support, processor deposits, and review-required items
- Tool-call discipline for accounting software workflows
- Useful as a specialist component in systems serving QuickBooks-style, Xero- style, FreshBooks-style, Sage-style, NetSuite-style, and Zoho-style workflows
- No autonomous posting, syncing, or write-back behavior by design
Intended Output Style
- choose
auto_ready,needs_attention, orblocked - emit source-linked JSON for review workflows
- prepare draft-only tool-call payloads for accounting software workflows
- keep posting/write-back actions behind validators and approval gates
Sanitized Example
Input:
Platform: cloud accounting system
Workflow: bank reconciliation
Context: imported bank transaction has partial supporting evidence
Expected behavior:
{
"decision": "needs_attention",
"evidence_class": "review_required",
"source_refs": [{"type": "bank_transaction", "id": "bank-demo-001"}],
"tool_calls": [
{
"tool": "ledgerguard.validate_draft",
"arguments": {
"case_id": "demo-001",
"draft_only": true,
"check_source_refs": true
}
}
],
"review_packet": {
"approval_required": true,
"draft_only": true,
"summary": "Route to accountant review before any posting action."
}
}
Plain English: produce review-ready draft outputs, preserve source references, and keep write/post/sync actions behind deterministic validators and approval.
Evaluation
This model was evaluated on BlackwoodAI internal, non-public accounting workflow tests focused on exact JSON/schema adherence, source-linked decisions, draft-only safety, and accounting software tool-call discipline. The evaluation data, generation process, baselines, and case counts are intentionally not disclosed.
License
See LICENSE. This release is personal/non-commercial only and prohibits
business use, hosted inference, SaaS, and model-as-a-service usage.
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