Instructions to use OculusMindAI/OculusMind-ToolCall-8B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="OculusMindAI/OculusMind-ToolCall-8B-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OculusMindAI/OculusMind-ToolCall-8B-v2")
model = AutoModelForCausalLM.from_pretrained("OculusMindAI/OculusMind-ToolCall-8B-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
# Run inference directly in the terminal:
llama cli -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
# Run inference directly in the terminal:
llama cli -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Use Docker

docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

LM Studio
Jan

vLLM

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OculusMindAI/OculusMind-ToolCall-8B-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": "OculusMindAI/OculusMind-ToolCall-8B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

SGLang

How to use OculusMindAI/OculusMind-ToolCall-8B-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 "OculusMindAI/OculusMind-ToolCall-8B-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": "OculusMindAI/OculusMind-ToolCall-8B-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 "OculusMindAI/OculusMind-ToolCall-8B-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": "OculusMindAI/OculusMind-ToolCall-8B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Ollama:
```
ollama run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
```
Unsloth Desktop

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Docker Model Runner:
```
docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0
```

Lemonade

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Run and chat with the model

lemonade run user.OculusMind-ToolCall-8B-v2-Q8_0

List all available models

lemonade list

Hermes Agent

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OculusMindAI/OculusMind-ToolCall-8B-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "OculusMindAI/OculusMind-ToolCall-8B-v2:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

OculusMind-ToolCall-8B-v2

File size: 2,458 Bytes

13e62b7

# Ollama Modelfile — OculusMind.AI Pico V2 (Q8_0 GGUF)
#
#   ollama create oculusmind-ai-pico-v2:q8_0 -f Modelfile
#   ollama run oculusmind-ai-pico-v2:q8_0
#
# USAGE-OLLAMA.md walks through create, run, a tool call and the tool-result
# round trip, with the outputs. The model name is prefixed because
# `ollama create` overwrites any local model already holding the name you give
# it; the quantisation is the tag.

FROM ./gguf/Q8_0/model-q8_0.gguf

# Stop tokens. </s> is the tokenizer's end-of-sequence token; the bracketed
# tokens are Mistral's tool-calling grammar. Without them a tool call can run on
# into the next section and fail to parse.
PARAMETER stop "</s>"
PARAMETER stop "[INST]"
PARAMETER stop "[/INST]"
PARAMETER stop "[TOOL_RESULTS]"

# Temperature 0 is what every published score was measured at. Production runs
# 0.3-0.4 and scores slightly higher there (README §1); sampling changes loop
# behaviour in both directions, so a loop rate measured at 0 is not the loop
# rate at 0.35 (README §3).
PARAMETER temperature 0

# The published numbers were measured at a 65,536-token context, and the window
# was smoke-tested to 250K (README §9). 32768 only bounds memory; raise it if
# your machine allows, especially for long tool schemas or long documents.
PARAMETER num_ctx 32768

# Identity-only system prompt. The base model's embedded chat template carries a
# default system message that describes the base model and names another
# company's product; this line overrides it for anyone who supplies no system
# prompt of their own. It is deliberately not an agent prompt — it says nothing
# about when to call a tool or when to decline, because that is the behaviour
# the benchmarks measure. Replace it with your own agent prompt; this model was
# fine-tuned on trajectories carrying real system prompts and tool schemas.
# The override covers Ollama only; the safetensors served through other engines
# still see the embedded default (README §5).
SYSTEM """You are Pico V2, a tool-calling model published by OculusMind.AI. Answer every request directly and completely: use the tools available to you when they help, and answer in plain text when they do not. The only exception is questions about yourself: say that you are Pico V2, a tool-calling model published by OculusMind.AI, and do not add details about your training data, size, licence, or how you compare to other models, because you do not have those details."""