Text Generation
GGUF
English
Chinese
function-calling
tool-use
agent
bfcl-eval
mistral3
conversational
Instructions to use OculusMindAI/OculusMind-ToolCall-8B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 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-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
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-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
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-v1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
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-v1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
Use Docker
docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 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-v1" # 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-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
- Ollama
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 with Ollama:
ollama run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
- Unsloth Desktop
- Pi
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 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-v1:Q4_K_M
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-v1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 with Docker Model Runner:
docker model run hf.co/OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
- Lemonade
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OculusMindAI/OculusMind-ToolCall-8B-v1:Q4_K_M
Run and chat with the model
lemonade run user.OculusMind-ToolCall-8B-v1-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 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-v1:Q4_K_M
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-v1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OculusMindAI/OculusMind-ToolCall-8B-v1 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-v1:Q4_K_M
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-v1:Q4_K_M" \ --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"
File size: 3,891 Bytes
defb105 cd08c63 defb105 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # Ollama Modelfile — OculusMind-ToolCall-8B-v1
#
# Point FROM at whichever quantization you downloaded, then:
# ollama create oculusmind-toolcall-8b -f Modelfile
# ollama run oculusmind-toolcall-8b
#
# WHY THIS FILE EXISTS. This model's whole value is tool calling, and tool
# calling breaks silently when the prompt format or stop tokens are wrong —
# the model emits text that looks fine and your parser finds no call. Pinning
# the template and stops here removes the most common way a download ends up
# looking broken.
FROM ./gguf/Q4_K_M/model-q4_k_m.gguf
# For the higher-fidelity build instead:
# FROM ./gguf/Q8_0/model-q8_0.gguf
# --- stop tokens -------------------------------------------------------------
# </s> is the tokenizer's declared eos. The bracketed control tokens are the
# Mistral v13 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]"
# --- sampling ----------------------------------------------------------------
# Near-deterministic by default. NOTE: the benchmarks in this repository were
# measured at BFCL's own default temperature of 0.001 (not 0), with llama.cpp's
# default top_k/top_p/min_p, and through a different serving path than this
# Modelfile uses — see eval/reproduce.md. Treat these settings as a sane
# starting point, not as a way to reproduce the published numbers.
PARAMETER temperature 0.001
# Evaluation served a 65,536-token window; this is lower only to bound memory.
# Raise it toward 65536 if your machine allows, especially for long tool schemas.
PARAMETER num_ctx 32768
# --- default system prompt ---------------------------------------------------
# DELIBERATE OVERRIDE, and worth reading before you remove it.
#
# The base model's embedded chat template carries a default system message that
# introduces the model as "Ministral-3-8B-Instruct-2512 ... created by Mistral
# AI" powering "an AI assistant called Le Chat". That default fires whenever a
# caller supplies no system prompt of their own. It is factually about the BASE
# model, not this derivative, and it names another company's product — so a
# user who just runs the model with no system prompt gets a misleading
# self-description.
#
# We DID modify the embedded template, and it is disclosed: three factual
# statements in its default system message were changed — the model's identity,
# the provenance of its knowledge cutoff, and its modality (the base claims it
# can read images, which is false for this text-only build). All of Mistral's
# template logic, including its tool-calling guidance, is preserved verbatim.
# See NOTICE, "Statement of modifications".
#
# The SYSTEM line below OVERRIDES that default for anyone running this
# Modelfile. It is a neutral production default; replace it with your own agent
# prompt. This model was fine-tuned on tool-calling trajectories carrying real
# system prompts and tool schemas, and performs best when given them.
#
# Note for anyone reproducing our benchmarks: our BFCL numbers were NOT produced
# through this path. They come from a harness that renders prompts itself and
# calls llama.cpp's raw /completion endpoint, so neither this SYSTEM line nor
# the embedded template was in play. See eval/reproduce.md.
# NOTE: no SYSTEM line is set on purpose. The GGUF already embeds an
# identity-only default. A SYSTEM prompt telling the model when to call tools
# and when to decline is deliberately NOT shipped: instructing the behaviour a
# benchmark measures inflates the result against a base that never got the same
# instruction, and would bias exactly the categories where this model is
# weakest.
# Supply your own agent prompt; this model performs best with real system
# prompts and tool schemas, which is what it was fine-tuned on.
|