Instructions to use SZLHOLDINGS/forge-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

betterwithage commited on 9 days ago

Commit

f9d9a6d

verified ·

1 Parent(s): f5f0b69

Unsloth Model Card

Browse files

Files changed (1) hide show

README.md +14 -14

README.md CHANGED Viewed

@@ -1,21 +1,21 @@
 ---
 license: apache-2.0
-library_name: transformers
-pipeline_tag: text-generation
-tags: [szl, forge-v3, not-trained, stephen-cto-seat]
 ---
-> **CARD honesty (SZL cookie):** This Hub entry is a **card / doctrine / kernel / witness** surface.
-> It does **not** claim production neural weights unless a MEASURED train receipt is linked below.
-> States used: `NOT_TRAINED` · `CARD_ONLY` · `MEASURED` · `UNAVAILABLE` · `NOT_CLAIMED`.
-> Production promote requires Stephen `GO WEIGHT <model-id>`. Soft Shield: Control before action. Evidence after.
-# forge-v3 — NOT_TRAINED card slot
-**Status: NOT_TRAINED**
-Reserved identity for Stephen Forge V3 CTO seat artifacts. This repository does not currently host trainable/deployable weights.
-House runtime for Forge V3 is separate from this Hub card.
-Do not claim WEIGHTS_OK against this id until a MEASURED train receipt + GO WEIGHT lands files here.
-Updated: 2026-11-04 (honesty pass).

 ---
+base_model: SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-sft-v3
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3_5
 license: apache-2.0
+language:
+- en
 ---
+# Uploaded finetuned  model
+- **Developed by:** SZLHOLDINGS
+- **License:** apache-2.0
+- **Finetuned from model :** SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-sft-v3
+This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)