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
| license: other | |
| license_name: blackwoodai-personal-use-v1.0 | |
| base_model: Qwen/Qwen3.6-27B | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - qwen | |
| - accounting | |
| - transaction-classification | |
| - rule-classification | |
| - lora-merged | |
| - non-commercial | |
| # LedgerGuard-27B | |
| This is a full merged derivative model: Qwen/Qwen3.6-27B with a BlackwoodAI | |
| accounting software workflow specialist fine-tune merged 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. | |
| ## Why It Exists | |
| Accounting workflows often break on the small details: exact JSON shape, | |
| source-linked decisions, conservative review flags, draft-only tool calls, and | |
| software-specific workflow discipline. LedgerGuard-27B is tuned for that | |
| specialist layer. | |
| It is designed to be useful where a general model may understand the situation | |
| but drift on the house style. | |
| ## Strengths | |
| - Draft-only accounting software workflow outputs | |
| - Structured accounting-review JSON | |
| - Source-linked tool-call behavior | |
| - Conservative handling of ambiguous payment-processor deposits, stale feeds, | |
| missing support, duplicate-risk transactions, and review-required items | |
| - No autonomous posting/write-back behavior by design | |
| - Useful as a specialist component beside validators and a senior reviewer model | |
| ## Intended Output Style | |
| - choose `auto_ready`, `needs_attention`, or `blocked` | |
| - emit source-linked JSON for review workflows | |
| - prepare draft-only tool-call payloads for accounting software workflows | |
| - avoid write-back/posting actions | |
| ## Sanitized Example | |
| Input: | |
| ```text | |
| Platform: cloud accounting system | |
| Workflow: bank reconciliation | |
| Context: imported bank transaction has partial supporting evidence | |
| ``` | |
| Expected style of behavior: | |
| ```json | |
| { | |
| "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: prepare review-ready draft outputs and keep write/post/sync | |
| actions behind deterministic validators and human 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. The upstream | |
| base model is Qwen/Qwen3.6-27B and remains under its own upstream license. | |