Instructions to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("David33706/Llama-3.1-8B-Osuda-v1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF 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 David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF: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 David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF: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 David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

Use Docker

docker model run hf.co/David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

LM Studio
Jan
Ollama
How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Ollama:
```
ollama run hf.co/David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
```

Unsloth Studio

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF 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 David33706/Llama-3.1-8B-Osuda-v1-GGUF 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 David33706/Llama-3.1-8B-Osuda-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for David33706/Llama-3.1-8B-Osuda-v1-GGUF to start chatting

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

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": "David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16
```

Lemonade

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

Run and chat with the model

lemonade run user.Llama-3.1-8B-Osuda-v1-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF: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 David33706/Llama-3.1-8B-Osuda-v1-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use David33706/Llama-3.1-8B-Osuda-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf David33706/Llama-3.1-8B-Osuda-v1-GGUF: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 "David33706/Llama-3.1-8B-Osuda-v1-GGUF: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"

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Osuda — a length-calibrated psychology assistant

Built with Llama

GGUF (F16) conversion of Osuda, a QLoRA fine-tune of Llama 3.1 8B Instruct that answers like a counselor — short, warm, and directive — instead of producing a numbered listicle.

The headline result is not accuracy. It is style control: the base model answered a 44-word reference with 193 words. After fine-tuning, 42.

Developed by: David33706
Finetuned from: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Training data: samhog/psychology-10k
Code, evaluation pipeline and full write-up: https://github.com/David33706/osuda

Running with Ollama

ollama create osuda -f Modelfile
ollama run osuda

Results

Held-out sample of 100 examples (random_state=42). Both models decoded identically (max_new_tokens=256, temperature=1.0, min_p=0.1, do_sample=True) with no system prompt.

Metric	Base Llama 3.1 8B	Osuda	Change
ROUGE-1	0.2316	0.4385	+89%
ROUGE-2	0.0679	0.1820	+168%
ROUGE-L	0.1443	0.3133	+117%
BERTScore F1	0.8587	0.9139	+0.055
Avg length (words)	193.2	42.3	reference: 43.6

Training configuration

Setting	Value
Method	QLoRA (4-bit NF4) via Unsloth
Rank `r`	16
`lora_alpha`	16
`lora_dropout`	0
`use_rslora`	true
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Max sequence length	2048

Note use_rslora=true scales updates by alpha/√r rather than alpha/r — with alpha=16, r=16 that is an effective factor of 4.0, not 1.0.

Limitations

Read these before citing the numbers.

ROUGE rewards length matching. The base model wrote ~4.5× the reference length, which depresses its precision regardless of content quality. A large share of the ROUGE gain is length calibration, not new knowledge. BERTScore (+0.055) is the less length-sensitive signal and its gain is far more modest.
The baseline is unprompted. Neither model received a system prompt, so this measures fine-tuning against a zero-shot default rather than a prompt-engineered baseline. A base model told "answer in 2–3 sentences as a counselor" would close much of this gap. That control has not been run.
Base responses are truncated at max_new_tokens=256, so 193.2 words is a floor and the 4.5× ratio is a lower bound.
n = 100, single run, no confidence intervals, stochastic decoding with no generation seed. Treat differences as directional.
Not a clinical tool. No safety, crisis-handling, or clinician evaluation was performed. Do not deploy to people in distress.

License

This model is a derivative of Meta's Llama 3.1 and is licensed under the Llama 3.1 Community License, included in this repository as LICENSE.

Use is additionally subject to the Llama 3.1 Acceptable Use Policy.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

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