Instructions to use togethercomputer/Tev1-4B-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/Tev1-4B-experimental with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/Tev1-4B-experimental")
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("togethercomputer/Tev1-4B-experimental")
model = AutoModelForMultimodalLM.from_pretrained("togethercomputer/Tev1-4B-experimental", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use togethercomputer/Tev1-4B-experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/Tev1-4B-experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/Tev1-4B-experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/togethercomputer/Tev1-4B-experimental

SGLang

How to use togethercomputer/Tev1-4B-experimental 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 "togethercomputer/Tev1-4B-experimental" \
    --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": "togethercomputer/Tev1-4B-experimental",
		"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 "togethercomputer/Tev1-4B-experimental" \
        --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": "togethercomputer/Tev1-4B-experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use togethercomputer/Tev1-4B-experimental with Docker Model Runner:
```
docker model run hf.co/togethercomputer/Tev1-4B-experimental
```

jianstreet commited on Sep 23

Commit

0b7becf

verified ·

1 Parent(s): eb67c1f

docs: move training resources below intro

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README.md +5 -5

README.md CHANGED Viewed

@@ -15,6 +15,11 @@ Tev1-4B-experimental is an experimental 4B decision model from Together AI. It i
 This is a Jev-inspired experiment, not a non-autoregressive Jev runtime. It retains Qwen’s standard next-token language-model head.
 ## Intended interface
 Provide a system instruction followed by a structured decision containing `state`, `question`, and 2–24 labeled `options`. The model should return exactly one option letter; application code maps that letter back to the semantic key.
@@ -82,11 +87,6 @@ These are development results, not an independent benchmark. The evaluation mixt
 - Prompt injection, multilingual behavior, calibration, and broad out-of-distribution robustness have not been comprehensively evaluated.
 - Local Transformers loading and exact environment requirements should be validated before relying on this checkpoint outside Together inference.
-## Resources
-- Learn how to train your own classifier for $17: https://www.together.ai/blog/how-to-train-your-own-jev
-- Full data recipe & code that we used to train Tev1: https://github.com/togethercomputer/tev1
 ## License
 The base Qwen3.5-4B model is Apache-2.0. The release license for these fine-tuned weights is being finalized before public conversion. Dataset sources retain their respective terms; the training mixture does not have a single blanket dataset license.

 This is a Jev-inspired experiment, not a non-autoregressive Jev runtime. It retains Qwen’s standard next-token language-model head.
+## Resources
+- Learn how to train your own classifier for $17: https://www.together.ai/blog/how-to-train-your-own-jev
+- Full data recipe & code that we used to train Tev1: https://github.com/togethercomputer/tev1
 ## Intended interface
 Provide a system instruction followed by a structured decision containing `state`, `question`, and 2–24 labeled `options`. The model should return exactly one option letter; application code maps that letter back to the semantic key.
 - Prompt injection, multilingual behavior, calibration, and broad out-of-distribution robustness have not been comprehensively evaluated.
 - Local Transformers loading and exact environment requirements should be validated before relying on this checkpoint outside Together inference.
 ## License
 The base Qwen3.5-4B model is Apache-2.0. The release license for these fine-tuned weights is being finalized before public conversion. Dataset sources retain their respective terms; the training mixture does not have a single blanket dataset license.