Instructions to use VertexResearch/Vertex-0.6-200M-Base-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexResearch/Vertex-0.6-200M-Base-Preview with Transformers:

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

pipe = pipeline("text-generation", model="VertexResearch/Vertex-0.6-200M-Base-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-200M-Base-Preview")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-200M-Base-Preview", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VertexResearch/Vertex-0.6-200M-Base-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-200M-Base-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexResearch/Vertex-0.6-200M-Base-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-200M-Base-Preview

SGLang

How to use VertexResearch/Vertex-0.6-200M-Base-Preview 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 "VertexResearch/Vertex-0.6-200M-Base-Preview" \
    --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": "VertexResearch/Vertex-0.6-200M-Base-Preview",
		"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 "VertexResearch/Vertex-0.6-200M-Base-Preview" \
        --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": "VertexResearch/Vertex-0.6-200M-Base-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VertexResearch/Vertex-0.6-200M-Base-Preview with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-200M-Base-Preview
```

Vertex 0.6 200M — Base (Training Preview)

An in-progress pretraining checkpoint of Vertex 0.6 200M, the largest model in the Vertex 0.6 family so far. This is a raw base model mid-way through its pretraining run — published as a preview so anyone can watch the model take shape. It is not the final base and has had no instruction tuning.

Progress: step 202,000 of 305,175 (~66% of the pretraining run).

Model details


Parameters	198.21M (tied embeddings)
Architecture	Qwen3-based transformer
Hidden size / layers	768 / 20
Attention	12 heads, 4 KV heads (GQA), head_dim 64
Context length	1024
Vocab	32768
Precision	bf16 training, fp32 export

Usage

Raw completion only — this is a base model, not a chat model.

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "VertexResearch/Vertex-0.6-200M-Base-Preview"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

ids = tok("The capital of France is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True,
                     temperature=0.6, top_p=0.9, repetition_penalty=1.3)
print(tok.decode(out[0], skip_special_tokens=True))

Limitations

A mid-training checkpoint: fluent English with reasonable structure, but facts degrade quickly beyond common knowledge, it has not seen dedicated code training, and outputs can drift or confabulate freely. No instruction following, no chat format, no safety tuning. Expect the final base and its instruct variants to improve substantially on this checkpoint.

Downloads last month: 297

Safetensors

Model size

0.2B params

Tensor type

F32