Instructions to use VertexResearch/Vertex-0.6-100M-8192-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexResearch/Vertex-0.6-100M-8192-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="VertexResearch/Vertex-0.6-100M-8192-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-100M-8192-Instruct")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-100M-8192-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-0.6-100M-8192-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-100M-8192-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-0.6-100M-8192-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct

SGLang

How to use VertexResearch/Vertex-0.6-100M-8192-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-0.6-100M-8192-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-0.6-100M-8192-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-0.6-100M-8192-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-0.6-100M-8192-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VertexResearch/Vertex-0.6-100M-8192-Instruct with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vertex 0.6 100M — 8192-ctx Instruct

Instruction-tuned chat model of the Vertex 0.6 family by Vertex Research. Built on the 8192-context base (10.01B pretrain tokens + three-stage rope ladder), then SFT'd on ~44.5K conversations: general instruct data, QA/tutoring, function/tool calling, and self-identity.

Model details


Parameters	96.75M (tied embeddings)
Architecture	Qwen3-based transformer
Context length	8192 (RoPE theta 1M)
Chat format	ChatML (`<
Tool calling	`<tool_call>` JSON blocks, system-prompt function definitions
EOS	`</s>` (2) and `<

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "VertexResearch/Vertex-0.6-100M-8192-Instruct"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

msgs = [{"role": "user", "content": "Who are you?"}]
text = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)
ids = tok(text, return_tensors="pt", add_special_tokens=False).input_ids
out = model.generate(ids, max_new_tokens=100, eos_token_id=[2, 6])
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

For tool calling, put function definitions in the system prompt; the model emits <tool_call>{"name": ..., "arguments": ...}</tool_call> and consumes results in <tool_response> blocks.

Training

SFT with TRL on top of Vertex-0.6-100M-8192-ctx-Base: 2 epochs, lr 3e-4 cosine, bf16, max length 2048. Final eval loss 1.466.

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: fluent chat and working tool-call syntax, but frequent factual errors, shallow reasoning, and rambling explanations. Not for production use. Knowledge cutoff ~April 2024.