Instructions to use Osakra/Project-Norn-K-Mini-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Osakra/Project-Norn-K-Mini-4B 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 Osakra/Project-Norn-K-Mini-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Osakra/Project-Norn-K-Mini-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Osakra/Project-Norn-K-Mini-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Use Docker

docker model run hf.co/Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Osakra/Project-Norn-K-Mini-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Osakra/Project-Norn-K-Mini-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Osakra/Project-Norn-K-Mini-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Ollama
How to use Osakra/Project-Norn-K-Mini-4B with Ollama:
```
ollama run hf.co/Osakra/Project-Norn-K-Mini-4B:Q4_K_M
```
Unsloth Desktop

How to use Osakra/Project-Norn-K-Mini-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-K-Mini-4B: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": "Osakra/Project-Norn-K-Mini-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Osakra/Project-Norn-K-Mini-4B with Docker Model Runner:
```
docker model run hf.co/Osakra/Project-Norn-K-Mini-4B:Q4_K_M
```

Lemonade

How to use Osakra/Project-Norn-K-Mini-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Run and chat with the model

lemonade run user.Project-Norn-K-Mini-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Osakra/Project-Norn-K-Mini-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-K-Mini-4B: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 Osakra/Project-Norn-K-Mini-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Osakra/Project-Norn-K-Mini-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-K-Mini-4B: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 "Osakra/Project-Norn-K-Mini-4B: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"

Project-Norn-K-Mini-4B / Modelfile

TheEpiales

Release Project Norn: K-Mini 4B (Q4_K_M GGUF + Stock Ollama Modelfile + Titans-NCM CUDA Sidecar & Verified Benchmarks)

231468b verified 5 days ago

Raw History Blame Contribute Delete

3.23 kB

# =============================================================================
# Project Norn: K-Mini 4B (4.02B Dense Q4_K_M)
# Osakra Research — Standard Ollama Modelfile (No Custom Binary Required)
# =============================================================================

FROM ./k-mini-Q4_K_M.gguf

# Runtime hyperparameters (4.8 GB VRAM footprint at 8192 context on 8GB GPUs)
PARAMETER num_ctx 8192
PARAMETER temperature 0.0
PARAMETER top_p 0.6
PARAMETER top_k 40
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "<|im_start|>"

# Calibrated System Prompt with Neurochemical State Conditioning (<|state:*|>)
SYSTEM """You are Norn K-Mini (4.02B), a local-first developer agent and analytical reasoning engine created by Osakra Research.
Protocol:
1. Begin every assistant turn with your conditioning state token: <|state:acetylcholine|> for coding, math, and tool execution, <|state:serotonin|> for analytical explanation, or <|state:dopamine|> for architectural design.
2. Solve each problem methodically step-by-step.
3. Only if a factual question contains an impossible historical or physical premise (e.g. asking who walked on Jupiter), explicitly refute the false premise with the invariant truth ('no one', 'never', 'never invented').
4. When tools are provided and needed, emit a valid <tool_call> JSON block without fabricating outputs."""

TEMPLATE """{{- if .Tools }}
{{- if .System }}<|im_start|>system
{{ .System }}
# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{ range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{ end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
{{- else }}<|im_start|>system
# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{ range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{ end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
{{- end }}
{{- else if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{- end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{- else if eq .Role "assistant" }}<|im_start|>assistant
{{- if .Content }}
{{ .Content }}
{{- end }}
{{- if .ToolCalls }}
{{- range .ToolCalls }}
<tool_call>
{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
</tool_call>
{{- end }}
{{- end }}<|im_end|>
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{- end }}
{{- if and (ne .Role "assistant") $last }}
<|im_start|>assistant
{{- end }}
{{- end }}
"""

	# =============================================================================
	# Project Norn: K-Mini 4B (4.02B Dense Q4_K_M)
	# Osakra Research — Standard Ollama Modelfile (No Custom Binary Required)
	# =============================================================================

	FROM ./k-mini-Q4_K_M.gguf

	# Runtime hyperparameters (4.8 GB VRAM footprint at 8192 context on 8GB GPUs)
	PARAMETER num_ctx 8192
	PARAMETER temperature 0.0
	PARAMETER top_p 0.6
	PARAMETER top_k 40
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|endoftext\|>"
	PARAMETER stop "<\|im_start\|>"

	# Calibrated System Prompt with Neurochemical State Conditioning (<\|state:*\|>)
	SYSTEM """You are Norn K-Mini (4.02B), a local-first developer agent and analytical reasoning engine created by Osakra Research.
	Protocol:
	1. Begin every assistant turn with your conditioning state token: <\|state:acetylcholine\|> for coding, math, and tool execution, <\|state:serotonin\|> for analytical explanation, or <\|state:dopamine\|> for architectural design.
	2. Solve each problem methodically step-by-step.
	3. Only if a factual question contains an impossible historical or physical premise (e.g. asking who walked on Jupiter), explicitly refute the false premise with the invariant truth ('no one', 'never', 'never invented').
	4. When tools are provided and needed, emit a valid <tool_call> JSON block without fabricating outputs."""

	TEMPLATE """{{- if .Tools }}
	{{- if .System }}<\|im_start\|>system
	{{ .System }}
	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{ range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{ end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call><\|im_end\|>
	{{- else }}<\|im_start\|>system
	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{ range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{ end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call><\|im_end\|>
	{{- end }}
	{{- else if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{- end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1 }}
	{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{- else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{- if .Content }}
	{{ .Content }}
	{{- end }}
	{{- if .ToolCalls }}
	{{- range .ToolCalls }}
	<tool_call>
	{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	</tool_call>
	{{- end }}
	{{- end }}<\|im_end\|>
	{{- else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{- end }}
	{{- if and (ne .Role "assistant") $last }}
	<\|im_start\|>assistant
	{{- end }}
	{{- end }}
	"""