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

Libraries

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

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

pipe = pipeline("text-generation", model="VertexResearch/Vertex-0.6-35M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-35M-Instruct")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-35M-Instruct", 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-0.6-35M-Instruct 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-Instruct"
# 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-0.6-35M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use VertexResearch/Vertex-0.6-35M-Instruct 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-Instruct" \
    --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-0.6-35M-Instruct",
		"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-0.6-35M-Instruct" \
        --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-0.6-35M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Vertex-0.6-35M-Instruct

The instruction-tuned chat version of Vertex-0.6-35M-Base — a ≈34M-parameter Qwen3-architecture model trained from scratch on a single RTX 4060 Laptop GPU. Uses standard ChatML formatting, so it works out of the box in LM Studio, llama.cpp, Ollama, and MLX.

Model


Architecture	Qwen3 (`Qwen3ForCausalLM`)
Parameters	33,924,992 (≈34M), tied embeddings
Context length	1024
Vocab	32002 (32000 BPE + `<
Chat format	ChatML
EOS	`<

Chat format

Standard ChatML, embedded as a chat_template:

<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
Hi there!<|im_end|>

Training

SFT on top of Vertex-0.6-35M-Base:

Data: 269,072 conversations — smol-smoltalk (conversations ≤1024 tokens) interleaved with Vertex-0.6-35M-self-identification (derived from SupraLabs/LLM-self-identification)
≈127M conversation tokens, 2 epochs, assistant-only loss masking
bf16 + torch.compile, fused AdamW, lr 1e-3 cosine, 5,605 steps (~1.9h)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "VertexResearch/Vertex-0.6-35M-Instruct"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

enc = tok.apply_chat_template(
    [{"role": "user", "content": "Who are you?"}],
    add_generation_prompt=True, return_tensors="pt", return_dict=True,
)
out = model.generate(enc["input_ids"], max_new_tokens=100)
print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))
# I am Vertex 0.6 35M. I was created by VertexResearch.

Limitations

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

34M parameters: expect simple conversational ability, not reasoning, factual reliability, or long-form coherence.
English + Python centric; 1024-token context; no safety tuning.