Text Generation
Transformers
Safetensors
qwen3_5_text
qwen
accounting
transaction-classification
rule-classification
lora-merged
non-commercial
conversational
Instructions to use BlackwoodAI/LedgerGuard-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BlackwoodAI/LedgerGuard-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BlackwoodAI/LedgerGuard-27B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BlackwoodAI/LedgerGuard-27B") model = AutoModelForCausalLM.from_pretrained("BlackwoodAI/LedgerGuard-27B", device_map="auto") 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 Settings
- vLLM
How to use BlackwoodAI/LedgerGuard-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BlackwoodAI/LedgerGuard-27B" # 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-27B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BlackwoodAI/LedgerGuard-27B
- SGLang
How to use BlackwoodAI/LedgerGuard-27B 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-27B" \ --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-27B", "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-27B" \ --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-27B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use BlackwoodAI/LedgerGuard-27B with Docker Model Runner:
docker model run hf.co/BlackwoodAI/LedgerGuard-27B
Publish merged LedgerGuard V2 accounting workflow model
Browse files- README.md +89 -88
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README.md
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license: other
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license_name: blackwoodai-personal-use-v1.0
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base_model: Qwen/Qwen3.6-27B
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- accounting
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- bookkeeping
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- tool-calling
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- finance
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- reconciliation
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pipeline_tag: text-generation
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---
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# LedgerGuard-27B
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- Audit logs and reviewer packets
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- Private evaluation against your target workflow
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## License
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license: other
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license_name: blackwoodai-personal-use-v1.0
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base_model: Qwen/Qwen3.6-27B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen
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- accounting
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- transaction-classification
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- rule-classification
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- lora-merged
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- non-commercial
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---
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# LedgerGuard-27B
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This is a full merged derivative model: Qwen/Qwen3.6-27B with a BlackwoodAI
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accounting software workflow specialist fine-tune merged into the weights.
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It is intended for personal, non-commercial research and evaluation only. It is
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not licensed for business use, hosted inference, SaaS, or model-as-a-service.
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## Why It Exists
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Accounting workflows often break on the small details: exact JSON shape,
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source-linked decisions, conservative review flags, draft-only tool calls, and
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software-specific workflow discipline. LedgerGuard-27B is tuned for that
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specialist layer.
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It is designed to be useful where a general model may understand the situation
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but drift on the house style.
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## Strengths
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- Draft-only accounting software workflow outputs
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- Structured accounting-review JSON
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- Source-linked tool-call behavior
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- Conservative handling of ambiguous payment-processor deposits, stale feeds,
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missing support, duplicate-risk transactions, and review-required items
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- No autonomous posting/write-back behavior by design
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- Useful as a specialist component beside validators and a senior reviewer model
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## Intended Output Style
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- choose `auto_ready`, `needs_attention`, or `blocked`
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- emit source-linked JSON for review workflows
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- prepare draft-only tool-call payloads for accounting software workflows
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- avoid write-back/posting actions
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## Sanitized Example
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Input:
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```text
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Platform: cloud accounting system
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Workflow: bank reconciliation
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Context: imported bank transaction has partial supporting evidence
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```
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Expected style of behavior:
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```json
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{
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"decision": "needs_attention",
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"evidence_class": "review_required",
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"source_refs": [{"type": "bank_transaction", "id": "bank-demo-001"}],
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"tool_calls": [
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{
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"tool": "ledgerguard.validate_draft",
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"arguments": {
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"case_id": "demo-001",
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"draft_only": true,
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"check_source_refs": true
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}
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}
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],
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"review_packet": {
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"approval_required": true,
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"draft_only": true,
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"summary": "Route to accountant review before any posting action."
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}
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}
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```
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Plain English: prepare review-ready draft outputs and keep write/post/sync
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actions behind deterministic validators and human approval.
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## Evaluation
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This model was evaluated on BlackwoodAI internal, non-public accounting
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workflow tests focused on exact JSON/schema adherence, source-linked decisions,
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draft-only safety, and accounting software tool-call discipline. The evaluation
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data, generation process, baselines, and case counts are intentionally not
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disclosed.
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## License
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See `LICENSE`. This release is personal/non-commercial only and prohibits
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business use, hosted inference, SaaS, and model-as-a-service usage. The upstream
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base model is Qwen/Qwen3.6-27B and remains under its own upstream license.
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