Instructions to use VertexResearch/Vertex-0.6-35M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexResearch/Vertex-0.6-35M-Base with Transformers:

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

pipe = pipeline("text-generation", model="VertexResearch/Vertex-0.6-35M-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-35M-Base")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-35M-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VertexResearch/Vertex-0.6-35M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-35M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexResearch/Vertex-0.6-35M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-35M-Base

SGLang

How to use VertexResearch/Vertex-0.6-35M-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-0.6-35M-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexResearch/Vertex-0.6-35M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-0.6-35M-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexResearch/Vertex-0.6-35M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VertexResearch/Vertex-0.6-35M-Base with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-35M-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vertex-0.6-35M-Base

A ≈34M-parameter base language model (Qwen3 architecture), pretrained from scratch on a single RTX 4060 Laptop GPU (8GB). Sibling of Vertex-0.6-100M, but fully independent: its own tokenizer, its own data pipeline, no shared weights or vocab.

Model


Architecture	Qwen3 (`Qwen3ForCausalLM`)
Parameters	33,924,224 (≈34M), tied embeddings
Hidden size	384
Layers	10
Attention heads	6 (GQA, 2 KV heads, head_dim 64)
Intermediate size	1536
Context length	1024
RoPE theta	10000
Vocab	32000, byte-level BPE

Tokenizer

Independent byte-level BPE (tokenizer.json), vocab 32000, trained on a ≈480K-document sample of the pretraining mix (≈85% web / ≈15% code). Special tokens: <|endoftext|> (bos/eos), <|pad|>. No chat/instruct special tokens — this is a raw base model.

Training data

≈1.50B unique tokens, trained for 2 epochs (≈3.0B tokens seen, ≈88 tokens/param):

Web (≈1.286B tokens, 85.7%): openbmb/Ultra-FineWeb-L1 (English, Apache-2.0)
Code (≈214M tokens, 14.3%): codeparrot/codeparrot-clean (deduplicated Python)

Documents were concatenated with <|endoftext|> separators and packed into fixed 1024-token blocks, shuffled independently each epoch.

Training recipe

Single epoch-pair on one RTX 4060 Laptop 8GB, bf16 autocast + torch.compile
AdamW (fused): lr 3e-3 peak, cosine decay to 10%, 300-step warmup, weight decay 0.1, betas (0.9, 0.95), grad clip 1.0
Batch: 8 x 1024 tokens x 12 grad-accum = 98,304 tokens/step
30,517 optimizer steps total, ≈50.8K tok/s sustained (≈16h wall clock)
Trained 2026-08-24 → 2026-08-25

Usage

from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast

model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-35M-Base")
tok = PreTrainedTokenizerFast(
    tokenizer_file=hf_hub_download("VertexResearch/Vertex-0.6-35M-Base", "tokenizer.json"),
    bos_token="<|endoftext|>", eos_token="<|endoftext|>", pad_token="<|pad|>",
)
out = model.generate(tok("The", return_tensors="pt").input_ids, max_new_tokens=50)
print(tok.decode(out[0]))

Notes

Base model only: no instruction tuning, no chat format, no safety tuning. For the instruction-tuned variant, see VertexResearch/Vertex-0.6-35M-Instruct.
English + Python centric; other languages are effectively untrained.

Limitations

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

Downloads last month: -

Safetensors

Model size

33.9M params

Tensor type

F32

Model tree for VertexResearch/Vertex-0.6-35M-Base

Finetunes

1 model

Quantizations

1 model

Datasets used to train VertexResearch/Vertex-0.6-35M-Base

Collection including VertexResearch/Vertex-0.6-35M-Base

Vertex 0.6

Collection

All Vertex 0.6 models • 10 items • Updated about 9 hours ago • 1