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
```

Vertex-0.6-35M-Instruct / README.md

Dalek70

Note coherence limitations

6d59f9d verified 7 days ago

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	---
	license: apache-2.0
	language:
	- en
	base_model: VertexResearch/Vertex-0.6-35M-Base
	datasets:
	- HuggingFaceTB/smol-smoltalk
	- VertexResearch/Vertex-0.6-35M-self-identification
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- chat
	- vertex
	- qwen3
	---

	# Vertex-0.6-35M-Instruct

	The instruction-tuned chat version of
	[Vertex-0.6-35M-Base](https://huggingface.co/VertexResearch/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 + `<\|im_start\|>`, `<\|im_end\|>`) \|
	\| Chat format \| ChatML \|
	\| EOS \| `<\|im_end\|>` \|

	## 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](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk)
	(conversations ≤1024 tokens) interleaved with
	[Vertex-0.6-35M-self-identification](https://huggingface.co/datasets/VertexResearch/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

	```python
	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.