Text Generation
Transformers
Safetensors
English
qwen3_5
image-text-to-text
kaiju-coder-7
coding
local-ai
business
opencode
tool-use
conversational
Instructions to use RMDWLLC/kaiju-coder-7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RMDWLLC/kaiju-coder-7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RMDWLLC/kaiju-coder-7") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("RMDWLLC/kaiju-coder-7") model = AutoModelForMultimodalLM.from_pretrained("RMDWLLC/kaiju-coder-7") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RMDWLLC/kaiju-coder-7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RMDWLLC/kaiju-coder-7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RMDWLLC/kaiju-coder-7", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RMDWLLC/kaiju-coder-7
- SGLang
How to use RMDWLLC/kaiju-coder-7 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 "RMDWLLC/kaiju-coder-7" \ --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": "RMDWLLC/kaiju-coder-7", "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 "RMDWLLC/kaiju-coder-7" \ --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": "RMDWLLC/kaiju-coder-7", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RMDWLLC/kaiju-coder-7 with Docker Model Runner:
docker model run hf.co/RMDWLLC/kaiju-coder-7
| # Kaiju Coder 7 by Kiyomi - Data Provenance Draft | |
| This draft records the current data boundary for release review. | |
| ## Policy | |
| Kaiju Coder training data must be legally usable for a commercial derivative model. | |
| Allowed: | |
| - RMDW-authored examples. | |
| - RMDW-owned repository diffs and documentation. | |
| - Human-reviewed examples created specifically for Kaiju. | |
| - Public permissive data only when license review confirms compatibility. | |
| Not allowed: | |
| - Closed-model answers from OpenAI, Anthropic, Gemini, or similar services as supervised completions. | |
| - Unreviewed customer data. | |
| - Private customer code without consent. | |
| - Secrets, tokens, credentials, cookies, or private keys. | |
| - Unlicensed scraped code. | |
| ## v0.1 Dataset Snapshot | |
| - Total reviewed examples: 575 | |
| - Dataset build: `datasets/build/kaiju-sft-v0.1.jsonl` | |
| - Candidate sources: | |
| - `datasets/candidates/rmdw-git-patches.jsonl` | |
| - `datasets/candidates/v0.1-safe-git-backlog.jsonl` | |
| - `datasets/candidates/v0.1-file-level-git.jsonl` | |
| - `datasets/candidates/v0.1-wiki-strategy-business-identity.jsonl` | |
| ## v1.7 Business-Owner Suite Addendum | |
| - Date prepared: 2026-06-03 | |
| - Reviewed examples: 8 | |
| - Candidate file: `datasets/candidates/v1.7-rmdw-business-owner-suite.jsonl` | |
| - Addendum-only SFT build: `datasets/build/kaiju-sft-v1.7-business-owner-suite.jsonl` | |
| - Training SFT build: `datasets/build/kaiju-sft-v1.7-business-owner-oversampled.jsonl` | |
| - Training config: `training/configs/qwen36-27b-lora-v1.7.example.json` | |
| - v1.8 training config: `training/configs/qwen36-27b-lora-v1.8-business-owner.example.json` | |
| - New task type: `business_suite` | |
| - Source inventory: `release/SOURCE_INVENTORY.md`, refreshed from GitHub source-of-truth repositories and the requested local RMDW wiki snapshot. | |
| This addendum targets Kiyomi 7.7.7 style business-owner work: complete AI-company build packs, premium service websites, intake and CRM flows, sales follow-up, proposals, ROI dashboards, operator handbooks, and Workshop golden-run automations. | |
| Every row includes: | |
| - `source_repos` | |
| - `source_paths` | |
| - `provenance_notes` | |
| - `reviewed: true` | |
| - `license: RMDW-owned` | |
| For the v1.7 LoRA run, the 8 reviewed business-owner rows are oversampled 24 times by `scripts/build_v17_business_owner_sft_dataset.py`. Repeated rows receive unique IDs ending in `__v17_business_repeat_NN` and preserve the original source repository, source path, and provenance metadata. | |
| Client-site repositories are used only as eval and generalized pattern sources unless a row is explicitly reviewed for training eligibility. Do not bulk-train on client-specific text, contact details, contracts, or private business data. | |
| The local wiki path `/Users/richardecholsai7/Documents/RMDW-Wiki` is present but is not a git checkout. It is recorded as `RMDW-Wiki-local`, `selective-reference-only`, with `credentials.md`, `customers.md`, `customers/`, and `raw/` excluded. The GitHub `RichardEchols/rmdw-agent-wiki` repo remains the authoritative wiki source for training/eval provenance unless a reviewer documents a local exception. | |
| ## Category Mix | |
| The v0.1 category gate passed: | |
| - Website/UI: at least 75 examples | |
| - Coding: at least 75 examples | |
| - Debugging: at least 50 examples | |
| - Automation: at least 50 examples | |
| - Tool-use: at least 50 examples | |
| - Strategy: at least 25 examples | |
| - Business: at least 15 examples | |
| - Identity: at least 10 examples | |
| ## Release Review Checklist | |
| Before public release: | |
| - Re-run dataset validation. | |
| - Re-run source inventory against the current GitHub source-of-truth SHAs. | |
| - Spot-check examples for secrets and private data. | |
| - Confirm client-site rows are generalized pattern examples or eval-only. | |
| - Confirm closed-model outputs are not used as supervised completions. | |
| - Record exact base model revision. | |
| - Attach upstream license and notices. | |
| - Attach eval summary. | |
| - Document known limitations and unsafe use boundaries. | |