Instructions to use nvidia/Nemotron-Labs-Diffusion-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Labs-Diffusion-14B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-Diffusion-14B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-Diffusion-14B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Nemotron-Labs-Diffusion-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Labs-Diffusion-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-Diffusion-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-14B

SGLang

How to use nvidia/Nemotron-Labs-Diffusion-14B 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 "nvidia/Nemotron-Labs-Diffusion-14B" \
    --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": "nvidia/Nemotron-Labs-Diffusion-14B",
		"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 "nvidia/Nemotron-Labs-Diffusion-14B" \
        --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": "nvidia/Nemotron-Labs-Diffusion-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Labs-Diffusion-14B with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-14B
```

YongganFu commited on 24 days ago

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@@ -3,197 +3,156 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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 tags: []
 ---
+# Nemotron-Diffusion-Exp-Ministral-14B-Instruct
+# Environment
+Docker path: `/lustre/fsw/portfolios/nvr/users/yongganf/docker/megatron_py25_dllm_ministral.sqsh` on CW-DFW. Apply for interactive nodes with the following command:
+```
+srun -A {account} --partition interactive --time 4:00:00 --gpus 8 --container-image /lustre/fsw/portfolios/nvr/users/yongganf/docker/megatron_py25_dllm_ministral.sqsh --container-mounts=$HOME:/home,/lustre:/lustre  --pty bash
+```
+## Chat with Our Model in dLM Mode
+```
+from transformers import AutoModel, AutoTokenizer
+import torch
+repo_name = "nvidia/Nemotron-Diffusion-Exp-Ministral-14B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
+model = model.cuda().to(torch.bfloat16)
+history = []
+user_input = input("User: ").strip()
+history.append({"role": "user", "content": user_input})
+prompt = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
+prompt_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device='cuda')
+out_ids, nfe = model.generate(prompt_ids, max_new_tokens=512, steps=512, block_length=32, shift_logits=False, causal_context=True, threshold=0.9, eos_token_id=tokenizer.eos_token_id)
+tokenized_out = tokenizer.batch_decode(out_ids[:, prompt_ids.shape[1]:], skip_special_tokens=True)[0]
+print(f"Model: {tokenized_out}")
+print(f"[Num Function Eval (NFE)={nfe}]")
+```
+## Chat with Our Model in AR Mode
+```
+from transformers import AutoModel, AutoTokenizer
+import torch
+repo_name = "nvidia/Nemotron-Diffusion-Exp-Ministral-14B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
+model = model.cuda().to(torch.bfloat16)
+history = []
+user_input = input("User: ").strip()
+history.append({"role": "user", "content": user_input})
+prompt = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
+prompt_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device='cuda')
+out_ids, nfe = model.ar_generate(inputs.input_ids, max_new_tokens=512)
+tokenized_out = tokenizer.batch_decode(out_ids[:, prompt_ids.shape[1]:], skip_special_tokens=True)[0]
+print(f"Model: {tokenized_out}")
+print(f"[Num Function Eval (NFE)={nfe}]")
+```
+## Chat with Our Model in Quadratic Self-Speculation Mode
+```
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+import torch
+repo_name = "nvidia/Nemotron-Diffusion-Exp-Ministral-14B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+config = AutoConfig.from_pretrained(repo_name, trust_remote_code=True)
+config.enable_self_spec = True
+model = AutoModel.from_pretrained(repo_name, config=config, trust_remote_code=True).cuda().to(torch.bfloat16)
+history = []
+user_input = input("User: ").strip()
+history.append({"role": "user", "content": user_input})
+prompt = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+inputs = inputs.to("cuda")
+out_ids, nfe = model.self_spec_generate(inputs.input_ids, max_new_tokens=512, steps=512, block_length=32, ar_mix_weight=0.5, eos_token_id=tokenizer.eos_token_id)
+tokenized_out = tokenizer.batch_decode(out_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
+print(f"Model: {tokenized_out}")
+print(f"[Num Function Eval (NFE)={nfe}]")
+```
+## Chat with Our Model in Linear Self-Speculation Mode
+```
+from transformers import AutoModel, AutoTokenizer
+import torch
+repo_name = "nvidia/Nemotron-Diffusion-Exp-Ministral-14B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
+model = model.cuda().to(torch.bfloat16)
+history = []
+user_input = input("User: ").strip()
+history.append({"role": "user", "content": user_input})
+prompt = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+prompt_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device='cuda')
+out_ids, nfe = model.linear_spec_generate(prompt_ids, max_new_tokens=512, block_length=32, eos_token_id=tokenizer.eos_token_id)
+tokenized_out = tokenizer.batch_decode(out_ids[:, prompt_ids.shape[1]:], skip_special_tokens=True)[0]
+print(f"Model: {tokenized_out}")
+print(f"[Num Function Eval (NFE)={nfe}]")
+```
+## Chat with Our Model in Linear Decoding Mode with Multi-Path Verification
+```
+from transformers import AutoModel, AutoTokenizer
+import torch
+repo_name = "nvidia/Nemotron-Diffusion-Exp-Ministral-14B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
+model = model.cuda().to(torch.bfloat16)
+history = []
+user_input = input("User: ").strip()
+history.append({"role": "user", "content": user_input})
+prompt = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+prompt_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device='cuda')
+out_ids, nfe = model.linear_spec_generate_mp(prompt_ids, max_new_tokens=512, block_length=32, eos_token_id=tokenizer.eos_token_id)
+tokenized_out = tokenizer.batch_decode(out_ids[:, prompt_ids.shape[1]:], skip_special_tokens=True)[0]
+print(f"Model: {tokenized_out}")
+print(f"[Num Function Eval (NFE)={nfe}]")
+```