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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="David33706/Llama-3.1-8B-Osuda-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("David33706/Llama-3.1-8B-Osuda-v1")
model = AutoModelForCausalLM.from_pretrained("David33706/Llama-3.1-8B-Osuda-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "David33706/Llama-3.1-8B-Osuda-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David33706/Llama-3.1-8B-Osuda-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "David33706/Llama-3.1-8B-Osuda-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David33706/Llama-3.1-8B-Osuda-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "David33706/Llama-3.1-8B-Osuda-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "David33706/Llama-3.1-8B-Osuda-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="David33706/Llama-3.1-8B-Osuda-v1",
    max_seq_length=2048,
)

Docker Model Runner
How to use David33706/Llama-3.1-8B-Osuda-v1 with Docker Model Runner:
```
docker model run hf.co/David33706/Llama-3.1-8B-Osuda-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Osuda — a length-calibrated psychology assistant

Built with Llama

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

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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Model tree for David33706/Llama-3.1-8B-Osuda-v1

Base model

meta-llama/Llama-3.1-8B

Finetuned

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unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit

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