Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
szl-holdings
doctrine-v11
launch-contract-governed
research-candidate
conversational
Instructions to use SZLHOLDINGS/forge-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SZLHOLDINGS/forge-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SZLHOLDINGS/forge-v3") 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("SZLHOLDINGS/forge-v3") model = AutoModelForMultimodalLM.from_pretrained("SZLHOLDINGS/forge-v3", device_map="auto") 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SZLHOLDINGS/forge-v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SZLHOLDINGS/forge-v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SZLHOLDINGS/forge-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SZLHOLDINGS/forge-v3
- SGLang
How to use SZLHOLDINGS/forge-v3 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 "SZLHOLDINGS/forge-v3" \ --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": "SZLHOLDINGS/forge-v3", "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 "SZLHOLDINGS/forge-v3" \ --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": "SZLHOLDINGS/forge-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SZLHOLDINGS/forge-v3 with Docker Model Runner:
docker model run hf.co/SZLHOLDINGS/forge-v3
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Download README.md from SZLHOLDINGS/forge-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.68 kB
-
https://huggingface.co/SZLHOLDINGS/forge-v3/resolve/main/README.md
- Command line
-
hf download hf://SZLHOLDINGS/forge-v3/README.md
-
curl -L -o README.md https://huggingface.co/SZLHOLDINGS/forge-v3/resolve/main/README.md
2.68 kB
| pipeline_tag: text-generation | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - szl-holdings | |
| - doctrine-v11 | |
| - launch-contract-governed | |
| - research-candidate | |
| base_model: SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-sft-v3 | |
| base_model_revision: 0340c300052e248367a3407aa493ef72a0da79fe | |
| <div align="center"> | |
| <img src="https://huggingface.co/SZLHOLDINGS/SZLHOLDINGS/resolve/main/assets/mark.svg" width="110" alt="SZL"/> | |
| # forge-v3 | |
| **Doctrine-corpus-v2 SFT student (estate doctrine, source-grounded)** | |
| `TRAINED THROUGH THE FAIL-CLOSED LAUNCH CONTRACT` · `RESEARCH_CANDIDATE` · `NOT_PROMOTED` | |
| </div> | |
| --- | |
| ## Proof of training | |
|  | |
| ## Lineage — every pin immutable | |
| | Stage | Artifact | Pin | | |
| |---|---|---| | |
| | Student base | `szl-receiptagent-qwen35-0.8b-sft-v3` | `0340c300` | | |
| | Corpus | Doctrine-corpus-v2 SFT student (estate doctrine, source-grounded) | group-disjoint split, seed 3407 | | |
| | Admission | operator receipt, `ADMITTED_FOR_TRAINING`, Apache 2.0 | on file | | |
| | Trainer | szl-forge governed pipeline | [launch contract #708](https://github.com/szl-holdings/szl-forge/pull/708) | | |
| | Receipts | [model-eval-receipts](https://huggingface.co/datasets/SZLHOLDINGS/model-eval-receipts) | per-wave | | |
| ## Honest status | |
| - ✅ Trained through the launch contract: immutable pins, disjoint holdout, corpus floor, admission receipt | |
| - ❌ Not promoted · not quality-certified · no broad benchmark | |
| - 📊 Evaluation on the group-disjoint holdout: pending publication | |
| <div align="center"><sub>SZL Holdings · Doctrine v11 LOCKED · failures stay published</sub></div> | |
| ## The estate | |
| | Surface | Link | | |
| |---|---| | |
| | Company | [szlholdings.com](https://szlholdings.com) | | |
| | Sponsors | [Become a sponsor](https://szlholdings.com/sponsors.html) | | |
| | GitHub | [szl-holdings](https://github.com/szl-holdings) | | |
| | Receipts chain | [frontier-54-runs](https://huggingface.co/datasets/SZLHOLDINGS/frontier-54-runs) | | |
| | Live dashboard | [SI command center](https://szlholdings-szlholdings-si.hf.space) | | |
| ## Card completeness (2026-11-12) | |
| This section adds required card sections the card above did not have. It states only what files in this repository and the estate's own receipt datasets show; nothing here is a new result. | |
| ### Evaluation | |
| Not yet evaluated. As of 2026-11-12 this repository has no evaluation results, battery receipts or `model-index` entries, so this card makes no quality, accuracy or safety claim. | |
| The estate model registry (`frontier-54-runs/registry/model-registry.json`, generated 2026-11-10) classifies this repo as `HOLD (weights, no provenance)`. | |