Instructions to use VertexResearch/Vertex-0.6-100M-1024-ctx-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexResearch/Vertex-0.6-100M-1024-ctx-Base with Transformers:

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

pipe = pipeline("text-generation", model="VertexResearch/Vertex-0.6-100M-1024-ctx-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-100M-1024-ctx-Base")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-100M-1024-ctx-Base", 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-0.6-100M-1024-ctx-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-100M-1024-ctx-Base"
# 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-0.6-100M-1024-ctx-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-100M-1024-ctx-Base

SGLang

How to use VertexResearch/Vertex-0.6-100M-1024-ctx-Base 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-0.6-100M-1024-ctx-Base" \
    --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-0.6-100M-1024-ctx-Base",
		"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-0.6-100M-1024-ctx-Base" \
        --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-0.6-100M-1024-ctx-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VertexResearch/Vertex-0.6-100M-1024-ctx-Base with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-100M-1024-ctx-Base
```

Vertex 0.6 100M — 1024-ctx Base

Base (text-completion) model of the Vertex 0.6 family by Vertex Research, pretrained from scratch on 10.01B tokens at 1024 context. This is the raw pretrain checkpoint: no instruction tuning, no chat alignment. Instruct and reasoning variants (with extended context) are released separately.

Model details


Parameters	96.75M (tied embeddings)
Architecture	Qwen3-based transformer
Hidden size / layers	576 / 16
Attention	9 heads, 3 KV heads (GQA), head_dim 64
FFN size	2304 (SwiGLU)
Context length	1024
RoPE theta	10,000
Vocab	32,768 (Supra2-100M tokenizer, digit-split)
Precision	trained fp32 compute-graph via torch.compile

Training

Data: ~10.01B tokens — English web text from Ultra-FineWeb plus a code slice, packed into 1024-token blocks, seeded shuffle, single epoch.
Schedule: 101,819 optimizer steps, global batch 98,304 tokens (12 × grad-accum 8 × 1024), LR 3e-3 cosine → 3e-4, 300 warmup steps.
Hardware: single RTX 4060 Ti 16GB, ~35K tokens/s, ~3.5 days wall clock.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-100M-1024-ctx-Base")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-100M-1024-ctx-Base")

ids = tok("The capital of France is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=30)
print(tok.decode(out[0]))

This is a completion model — prompt it with text to continue, not with chat messages.

Limitations

These models are not the most coherent yet and need more tuning: expect rambling, repetition, and inconsistent answers, especially over longer generations.

97M parameters and 10B tokens: expect fluent English and basic factual/code completion, frequent factual errors, and repetition under greedy decoding. Knowledge cutoff of the web corpus is approximately April 2024.

Downloads last month: 161

Safetensors

Model size

96.8M params

Tensor type

F32

Dataset used to train VertexResearch/Vertex-0.6-100M-1024-ctx-Base

Collection including VertexResearch/Vertex-0.6-100M-1024-ctx-Base

Vertex 0.6

Collection

All Vertex 0.6 models • 10 items • Updated about 18 hours ago • 1