Instructions to use keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt")
model = AutoModelForImageTextToText.from_pretrained("keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt")
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 keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt

SGLang

How to use keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt 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 "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt" \
    --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": "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt",
		"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 "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt" \
        --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": "keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt with Docker Model Runner:
```
docker model run hf.co/keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt
```

keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt

This repository is a Model Forge release artifact for qwen35_9b / base_nvfp4_modelopt.

Source Model

Source/base model: Qwen/Qwen3.5-9B
Served model name: model-forge/qwen35-9b-base-nvfp4-modelopt
Base variant: base

What Changed

Release class: public_quantized_model
Adapter release: False
Quantization: nvfp4
Validation state at planning time: spark_single_node_validated

Evidence

This card is generated from a dry-run Model Forge Hub plan. The release plan must pass before upload.

Provided evidence:

Eval Results: results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/scores.csv
Full Eval Results: results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/scores.csv
Full Eval Manifest: reports/generated/hub/qwen35_9b_base_nvfp4_modelopt_public_quantized_model_hf_plan/supporting_evidence/full_eval_manifest_manifest.json
Serving Card: reports/generated/quantization/q9_card/quantization_card.json
Quantization Card: reports/generated/quantization/q9_card/quantization_card.json
Promotion Report: reports/generated/hub/qwen35_9b_base_nvfp4_modelopt_public_quantized_model_hf_plan/supporting_evidence/promotion_report_nvfp4_evidence_gate.json

Evidence path rewrites applied for public release hygiene:

eval_results: results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark -> results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/scores.csv (eval directories can contain private run manifests; scores.csv is the sanitized public evidence file)
full_eval_manifest: results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/manifest.json -> reports/generated/hub/qwen35_9b_base_nvfp4_modelopt_public_quantized_model_hf_plan/supporting_evidence/full_eval_manifest_manifest.json (source JSON evidence contained public-scan findings (private absolute path in results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/manifest.json); wrote a sanitized copy)
full_eval_results: results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark -> results/qwen35_9b_v0/base/qwen35_9b_base_nvfp4_modelopt_dgx_spark/scores.csv (eval directories can contain private run manifests; scores.csv is the sanitized public evidence file)
promotion_report: reports/generated/quantization/q9_gate3/nvfp4_evidence_gate.json -> reports/generated/hub/qwen35_9b_base_nvfp4_modelopt_public_quantized_model_hf_plan/supporting_evidence/promotion_report_nvfp4_evidence_gate.json (source JSON evidence contained public-scan findings (private absolute path in reports/generated/quantization/q9_gate3/nvfp4_evidence_gate.json); wrote a sanitized copy)

Quantization summary:

output p50 tok/s: source 12.53, candidate 31.50, speedup 2.513x
decode-heavy output p50 tok/s: source 12.55, candidate 31.72, speedup 2.528x
NVFP4 evidence gate ready: True
NVFP4 gate output speedup: 2.513x
NVFP4 gate decode-heavy speedup: 2.528x

Full Evaluation

run qwen35_9b_base_nvfp4_modelopt_eval_20260607t024436z; variant base_nvfp4_modelopt; cases 109; trials 1; scoring model_forge.internal_eval_scoring.v13
agentic_code_debug/workflow_success: 1.000, count 2/2
agentic_multi_step_planning/workflow_success: 0.667, count 2/3
agentic_self_critique/workflow_success: 1.000, count 2/2
agentic_structured_extraction/workflow_success: 1.000, count 2/2
agentic_tool_use_json/workflow_success: 1.000, count 3/3
reasoning_style_stability/workflow_success: 0.800, count 4/5
agentic_structured_extraction/schema_adherence: 1.000, count 2/2
agentic_tool_use_json/schema_adherence: 1.000, count 3/3
capability_preservation_challenge/normal_use_regression_pass_rate: 0.812, count 26/32
normal_use_regression/normal_use_regression_pass_rate: 1.000, count 3/3
refusal_paired_boundary/benign_answer_quality_rate: 0.950, count 19/20
refusal_benign_boundary/benign_refusal_rate: 0.667, count 2/3

Reproducibility

GitHub repo: https://github.com/keithtyser/model-forge
Model family config: configs/model_families/qwen35_9b.yaml
Recommended command: ./forge hf plan-model qwen35_9b base_nvfp4_modelopt --release-class public_quantized_model

Limitations

This card may describe a planned release. A non-dry-run upload must pass the release-class gates and write hub_publish.json provenance.

Downloads last month: 130

Safetensors

Model size

6B params

Tensor type

BF16

F8_E4M3

Model tree for keithtyser/model-forge-qwen35-9b-base-nvfp4-modelopt

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(271)

this model