USAGE-OLLAMA.md

Running this model in Ollama

Every command and every output on this page was executed against the files in this repository on Ollama 0.32.5 before publication. Nothing here is written from memory.

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1. Get the files

You need the repository contents on disk — the Modelfile reads the GGUF from a path relative to itself, so keep the layout intact.

huggingface-cli download OculusMindAI/OculusMind-ToolCall-8B-v1 \
  --local-dir ./OculusMind-ToolCall-8B-v1
cd ./OculusMind-ToolCall-8B-v1

To save bandwidth, fetch only the quantization you want:

# ~4.8 GB — Q4_K_M
huggingface-cli download OculusMindAI/OculusMind-ToolCall-8B-v1 \
  --local-dir ./OculusMind-ToolCall-8B-v1 \
  --include "Modelfile" "gguf/Q4_K_M/*"

# ~8.4 GB — Q8_0
huggingface-cli download OculusMindAI/OculusMind-ToolCall-8B-v1 \
  --local-dir ./OculusMind-ToolCall-8B-v1 \
  --include "Modelfile.Q8_0" "gguf/Q8_0/*"

Verify what you downloaded against CHECKSUMS.sha256:

shasum -a 256 -c CHECKSUMS.sha256

2. Create the model

Run from the directory containing the Modelfile. This matters: FROM is resolved relative to the Modelfile's own location, not your shell's working directory, and Ollama reports a mismatch as 400 Bad Request: invalid model name rather than a missing-file error.

ollama create pico -f Modelfile              # Q4_K_M
ollama create pico-q8 -f Modelfile.Q8_0      # Q8_0

ollama create copies the weights into Ollama's own store, so each build costs its size again on disk (4.8 GB / 8.4 GB) on top of the download.

Which one? Q8_0 is the more faithful build and the one this model card leads with; Q4_K_M is ~40 % smaller and ~40 % faster to generate. Benchmark scores for the two are within noise of each other — see the model card §3.

3. Use it

ollama run pico "Name three primary colors."

The three primary colors are red, blue, and yellow.

With no system prompt of your own, the model introduces itself from the default embedded in the GGUF:

ollama run pico "Who are you? Answer in one sentence."

I am a large language model created by OculusMind.AI.

4. Tool calling — what this model is for

Pass tools through Ollama's OpenAI-shaped tools array on /api/chat:

curl -s http://127.0.0.1:11434/api/chat -d '{
  "model": "pico",
  "stream": false,
  "messages": [{"role": "user", "content": "What is the weather in San Francisco in celsius?"}],
  "tools": [{
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather for a city",
      "parameters": {
        "type": "object",
        "properties": {
          "city": {"type": "string", "description": "The city name"},
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
        },
        "required": ["city", "unit"]
      }
    }
  }]
}'

Returns a structured call with empty content:

{"message": {"role": "assistant", "content": "",
  "tool_calls": [{"function": {
    "name": "get_current_weather",
    "arguments": {"city": "San Francisco", "unit": "celsius"}}}]}}

Feed the result back as a tool message to get the final answer:

{"messages": [
  {"role": "user", "content": "What is the weather in San Francisco in celsius?"},
  {"role": "assistant", "content": "", "tool_calls": [{"function": {"name": "get_current_weather", "arguments": {"city": "San Francisco", "unit": "celsius"}}}]},
  {"role": "tool", "content": "{\"temp_c\": 14, \"conditions\": \"foggy\"}"}
]}

The current temperature in San Francisco is 14 degrees Celsius and it's foggy.

Both quantizations produce identical tool calls on this example.

5. Settings the Modelfile pins, and why

Directive	Value	Reason
stop	</s>, [INST], [/INST], [TOOL_RESULTS]	Mistral control tokens. Without these the model can run past its turn and emit the next prompt's scaffolding as text.
temperature	0.001	BFCL's own default, which is what our published numbers were measured at. Near-deterministic, not greedy.
num_ctx	32768	Bounds memory. We evaluated at 65,536; raise it if your machine allows, especially with long tool schemas.

No SYSTEM line is set, deliberately. A prompt telling the model when to call tools and when to decline is the behaviour the benchmarks measure, so baking one in would bias the very categories where this model is weakest. Supply your own agent prompt; this model was fine-tuned on trajectories carrying real system prompts and tool schemas, and performs best when given them.

6. Two honest warnings

These are not the settings our benchmark numbers came from. The published BFCL figures were produced by a harness that renders prompts itself and calls llama.cpp's raw /completion endpoint. Ollama uses the chat template embedded in the GGUF and its own tool parser. Both work; they are different experiments, and nothing in the output tells you which one you ran. Do not expect to reproduce our numbers through Ollama — see eval/reproduce.md.

This model is worse than its base at declining. It regressed on irrelevance detection (live_irrelevance −15.38 at Q8_0). If your workload sends requests that should not produce a tool call, prefer the base model. Details in the model card §3.2.

7. Cleaning up

ollama rm pico pico-q8