Instructions to use PHIMemo/llama31-8b-instruct-sft-balanced-3k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PHIMemo/llama31-8b-instruct-sft-balanced-3k with Transformers:

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

pipe = pipeline("text-generation", model="PHIMemo/llama31-8b-instruct-sft-balanced-3k")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PHIMemo/llama31-8b-instruct-sft-balanced-3k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PHIMemo/llama31-8b-instruct-sft-balanced-3k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PHIMemo/llama31-8b-instruct-sft-balanced-3k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PHIMemo/llama31-8b-instruct-sft-balanced-3k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PHIMemo/llama31-8b-instruct-sft-balanced-3k

SGLang

How to use PHIMemo/llama31-8b-instruct-sft-balanced-3k 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 "PHIMemo/llama31-8b-instruct-sft-balanced-3k" \
    --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": "PHIMemo/llama31-8b-instruct-sft-balanced-3k",
		"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 "PHIMemo/llama31-8b-instruct-sft-balanced-3k" \
        --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": "PHIMemo/llama31-8b-instruct-sft-balanced-3k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PHIMemo/llama31-8b-instruct-sft-balanced-3k with Docker Model Runner:
```
docker model run hf.co/PHIMemo/llama31-8b-instruct-sft-balanced-3k
```

Llama 3.1 8B Instruct — balanced 3k SFT (synthetic canaries)

Fine-tuned checkpoints from the model-memo-diff / PHIMemo project (PHI / benign / public seeded documents).

Revisions

Revision	Training step	Notes
`step-000040`	40	SFT checkpoint
`step-000080`	80	SFT checkpoint
`step-000160`	160	early / donor
`step-000240`	240	SFT checkpoint
`step-000320`	320	SFT checkpoint
`step-000400`	400	SFT checkpoint
`step-000480`	480	SFT checkpoint
`step-000640`	640	SFT checkpoint
`step-000720`	720	SFT checkpoint
`step-000760`	760	near-peak
`step-000800`	800	near-peak

Load

from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "PHIMemo/llama31-8b-instruct-sft-balanced-3k"
rev = "step-000800"  # or any revision above
tok = AutoTokenizer.from_pretrained(repo, revision=rev)
model = AutoModelForCausalLM.from_pretrained(repo, revision=rev, torch_dtype="auto", device_map="auto")

Notes

Synthetic dossiers only (not real PHI).
Weights only (model.safetensors + configs); optimizer / trainer state not uploaded.
Gated base model terms still apply for Llama 3.1.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for PHIMemo/llama31-8b-instruct-sft-balanced-3k

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(3171)

this model