Instructions to use haphazardlyinc/TestModelV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use haphazardlyinc/TestModelV1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="haphazardlyinc/TestModelV1",
	filename="Final_Merged-1.1B-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use haphazardlyinc/TestModelV1 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 haphazardlyinc/TestModelV1:F16
# Run inference directly in the terminal:
llama cli -hf haphazardlyinc/TestModelV1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf haphazardlyinc/TestModelV1:F16
# Run inference directly in the terminal:
llama cli -hf haphazardlyinc/TestModelV1:F16

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 haphazardlyinc/TestModelV1:F16
# Run inference directly in the terminal:
./llama-cli -hf haphazardlyinc/TestModelV1:F16

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 haphazardlyinc/TestModelV1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf haphazardlyinc/TestModelV1:F16

Use Docker

docker model run hf.co/haphazardlyinc/TestModelV1:F16

LM Studio
Jan
Ollama
How to use haphazardlyinc/TestModelV1 with Ollama:
```
ollama run hf.co/haphazardlyinc/TestModelV1:F16
```

Unsloth Studio

How to use haphazardlyinc/TestModelV1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for haphazardlyinc/TestModelV1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for haphazardlyinc/TestModelV1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for haphazardlyinc/TestModelV1 to start chatting

How to use haphazardlyinc/TestModelV1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf haphazardlyinc/TestModelV1:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "haphazardlyinc/TestModelV1:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use haphazardlyinc/TestModelV1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf haphazardlyinc/TestModelV1:F16

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 haphazardlyinc/TestModelV1:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use haphazardlyinc/TestModelV1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf haphazardlyinc/TestModelV1:F16

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 "haphazardlyinc/TestModelV1:F16" \
  --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"

Docker Model Runner
How to use haphazardlyinc/TestModelV1 with Docker Model Runner:
```
docker model run hf.co/haphazardlyinc/TestModelV1:F16
```

Lemonade

How to use haphazardlyinc/TestModelV1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull haphazardlyinc/TestModelV1:F16

Run and chat with the model

lemonade run user.TestModelV1-F16

List all available models

lemonade list

haphazardlyinc commited on Jun 4

Commit

3b0ccd4

verified ·

1 Parent(s): 98fbf4d

Update README.md

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@@ -164,7 +164,63 @@ Example behaviour:
 Then gradually builds toward the explanation.
 ---
 ## 🧩 Tool Use (Optional)
 This model may be integrated with external tools such as:

 Then gradually builds toward the explanation.
 ---
+## Evaluation Results
+Evaluated using the EleutherAI LM Evaluation Harness (`lm-eval`) on a Radeon RX 7900 XTX.
+### Core Benchmarks
+| Benchmark | Score |
+|------------|--------:|
+| MMLU-Pro (5-shot) | 27.91% |
+| GSM8K (5-shot) | 39.88% |
+| ARC-Challenge | 33.62% |
+| HellaSwag | 38.00% |
+| HellaSwag (Norm) | 48.37% |
+| Winogrande | 57.22% |
+### MMLU-Pro Breakdown
+| Subject | Score |
+|----------|-------:|
+| Biology | 48.26% |
+| Psychology | 42.11% |
+| Economics | 38.63% |
+| Math | 38.34% |
+| Computer Science | 30.49% |
+| Philosophy | 28.46% |
+| Health | 28.24% |
+| Business | 27.50% |
+| Other | 27.16% |
+| Physics | 22.56% |
+| History | 21.78% |
+| Chemistry | 16.17% |
+| Engineering | 15.79% |
+| Law | 13.99% |
+### Evaluation Command
+```bash
+lm-eval run \
+  --model hf \
+  --model_args pretrained=<model_path> \
+  --tasks mmlu_pro,gsm8k,hellaswag,winogrande,arc_challenge \
+  --device cuda:0
+```
+### Notes
+These benchmarks were obtained after multi-stage fine-tuning on Socratic dialogue, reasoning, reflective thinking, educational tutoring, tool-calling, and conversational support datasets.
+The model is optimized for:
+- Educational tutoring
+- Socratic questioning
+- Guided reasoning
+- Critical thinking
+- Research assistance
+rather than direct-answer benchmark optimization.
+```
 ## 🧩 Tool Use (Optional)
 This model may be integrated with external tools such as: