Instructions to use r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42 with Transformers:

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

pipe = pipeline("text-generation", model="r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42")
model = AutoModelForCausalLM.from_pretrained("r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42

SGLang

How to use r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42 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 "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42" \
    --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": "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42",
		"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 "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42" \
        --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": "r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42 with Docker Model Runner:
```
docker model run hf.co/r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42
```

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llama-3.2-1B-bio-kg-n200-m3-seed42

Model Description

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on biographical knowledge graph data for knowledge completion tasks.

Training Data

Dataset: Biographical KG (n=200, m=3, seed=42)
Domain: Biographical knowledge graphs
Task: Causal language modeling for knowledge completion
Data Type: Synthetic data generated from knowledge graph triples

Training Details

Training Parameters

Epochs: 20
Batch Size: 32
Learning Rate: 5e-05
Lr Scheduler: cosine_with_min_lr
Nodes: 200
Edges Per Node: 3
Random Seed: 42

Base Model

Model: meta-llama/Llama-3.2-1B
Architecture: Transformer-based causal language model

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42")
tokenizer = AutoTokenizer.from_pretrained("r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42")

# Example: Knowledge completion
input_text = "Albert Einstein was born in"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate completion
outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=False,
    pad_token_id=tokenizer.pad_token_id
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

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Safetensors

Model size

1B params

Tensor type

F32

Model tree for r-takahashi/llama-3.2-1B-bio-kg-n200-m3-seed42

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