Instructions to use MangoLassi/llama-3.2-1b-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MangoLassi/llama-3.2-1b-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="MangoLassi/llama-3.2-1b-finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MangoLassi/llama-3.2-1b-finetuned")
model = AutoModelForCausalLM.from_pretrained("MangoLassi/llama-3.2-1b-finetuned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MangoLassi/llama-3.2-1b-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MangoLassi/llama-3.2-1b-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MangoLassi/llama-3.2-1b-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MangoLassi/llama-3.2-1b-finetuned

SGLang

How to use MangoLassi/llama-3.2-1b-finetuned 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 "MangoLassi/llama-3.2-1b-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MangoLassi/llama-3.2-1b-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MangoLassi/llama-3.2-1b-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MangoLassi/llama-3.2-1b-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MangoLassi/llama-3.2-1b-finetuned with Docker Model Runner:
```
docker model run hf.co/MangoLassi/llama-3.2-1b-finetuned
```

A newer version of this model is available: meta-llama/Llama-3.2-1B

Fine-tuned Llama-3.2-1B Model

This is a fine-tuned version of meta-llama/Llama-3.2-1B with PEFT/LoRA.

Model Details

Base model: meta-llama/Llama-3.2-1B
Fine-tuning method: PEFT with LoRA
Rank (r): 8
Alpha: 16
Target modules: q_proj, v_proj

Usage

from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM

# Load model and tokenizer
model = AutoPeftModelForCausalLM.from_pretrained("MangoLassi/llama-3.2-1b-finetuned")
tokenizer = AutoTokenizer.from_pretrained("MangoLassi/llama-3.2-1b-finetuned")

# Generate text
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 2

Model tree for MangoLassi/llama-3.2-1b-finetuned

Base model

meta-llama/Llama-3.2-1B

Quantized

(243)

this model

MangoLassi
/

llama-3.2-1b-finetuned

Fine-tuned Llama-3.2-1B Model

Model Details

Usage

Model tree for MangoLassi/llama-3.2-1b-finetuned

Dataset used to train MangoLassi/llama-3.2-1b-finetuned