Instructions to use PoSTMEDIA/Rosetta-7B-Think-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PoSTMEDIA/Rosetta-7B-Think-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="PoSTMEDIA/Rosetta-7B-Think-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PoSTMEDIA/Rosetta-7B-Think-NVFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PoSTMEDIA/Rosetta-7B-Think-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PoSTMEDIA/Rosetta-7B-Think-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PoSTMEDIA/Rosetta-7B-Think-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PoSTMEDIA/Rosetta-7B-Think-NVFP4

SGLang

How to use PoSTMEDIA/Rosetta-7B-Think-NVFP4 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 "PoSTMEDIA/Rosetta-7B-Think-NVFP4" \
    --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": "PoSTMEDIA/Rosetta-7B-Think-NVFP4",
		"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 "PoSTMEDIA/Rosetta-7B-Think-NVFP4" \
        --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": "PoSTMEDIA/Rosetta-7B-Think-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PoSTMEDIA/Rosetta-7B-Think-NVFP4 with Docker Model Runner:
```
docker model run hf.co/PoSTMEDIA/Rosetta-7B-Think-NVFP4
```

Rosetta-7B-Think-NVFP4 / generation_config.json

PoSTMEDIA

Add Rosetta-7B-Think-NVFP4 (NVFP4, TensorRT Model Optimizer 0.46.1)

cbb2c26 verified 2 months ago

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222 Bytes

{
  "_from_model_config": true,
  "do_sample": true,
  "eos_token_id": [
    100265,
    100257
  ],
  "output_attentions": false,
  "output_hidden_states": false,
  "transformers_version": "5.14.1",
  "use_cache": true
}

	{
	"_from_model_config": true,
	"do_sample": true,
	"eos_token_id": [
	100265,
	100257
	],
	"output_attentions": false,
	"output_hidden_states": false,
	"transformers_version": "5.14.1",
	"use_cache": true
	}