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"

Llama-3.1-8B-Osuda-v1-GGUF / README.md

David33706

Fix license: Llama 3.1 Community License, add Built with Llama attribution

8960065 verified 23 days ago

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	---
	base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
	language:
	- en
	license: llama3.1
	tags:
	- text-generation-inference
	- transformers
	- gguf
	- unsloth
	- llama
	- trl
	- psychology
	- counseling
	---

	# 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`](https://huggingface.co/datasets/samhog/psychology-10k)
	- Code, evaluation pipeline and full write-up: https://github.com/David33706/osuda

	## Running with Ollama

	```bash
	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](https://github.com/unslothai/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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE),
	included in this repository as `LICENSE`.

	Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc.
	All Rights Reserved.

	Use is additionally subject to the [Llama 3.1 Acceptable Use Policy](https://llama.meta.com/llama3_1/use-policy).