Instructions to use VertexResearch/Vertex-0.6-100M-8192-Instruct-v2 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-v2 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-v2")
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-v2")
model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-100M-8192-Instruct-v2", 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-v2 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-v2"
# 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-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-v2

SGLang

How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-v2 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-v2" \
    --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-v2",
		"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-v2" \
        --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-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-v2 with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vertex 0.6 100M — 8192-ctx Instruct v2

Instruction-tuned chat model of the Vertex 0.6 family by Vertex Research, and the most capable chat model in the family so far. Built on a quality-annealed 8192-context base, then SFT'd on ~201K conversations weighted toward multi-turn dialogue so it holds a conversation across several turns instead of unravelling after the first message.

Model details


Parameters	96.75M (tied embeddings)
Architecture	Qwen3-based transformer
Context length	8192 (RoPE theta 1M)
Chat format	ChatML (`<\|im_start\|>` / `<\|im_end\|>`)
Tool calling	`<tool_call>` JSON blocks, system-prompt function definitions
EOS	`</s>` (2) and `<\|im_end\|>` (6)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "VertexResearch/Vertex-0.6-100M-8192-Instruct-v2"
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=120, 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

Base: an 8192-context Vertex 0.6 100M base given a 1B-token quality anneal (FineWeb-Edu + synthetic elementary word-problem math). SFT with TRL on ~201K conversations: smol-smoltalk (~130K, concise multi-turn), UltraChat 200k (~40K, longer multi-turn), everyday-conversations (basic-chat grounding, 2×), function/tool-calling data, QA/tutoring, and self-identity. 2 epochs, lr 3e-4 cosine, bf16, max length 2048. Final eval loss 1.404.

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: multi-turn chat is much improved and tool-call syntax works, but factual accuracy is low, reasoning is shallow, and it makes arithmetic errors. Not for production use. Knowledge cutoff ~April 2024.

Tool calling works mechanically (correct <tool_call> format, stops cleanly) with a single available function, but multi-step tool use is unreliable: with more than one function available it can pick the wrong tool, and it can hallucinate details when summarizing a tool's response rather than reporting it accurately. Don't trust it in an unsupervised agent loop.