Instructions to use comradepenguin1917/backward-llama2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use comradepenguin1917/backward-llama2-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
model = PeftModel.from_pretrained(base_model, "comradepenguin1917/backward-llama2-lora")

Transformers

How to use comradepenguin1917/backward-llama2-lora with Transformers:

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

pipe = pipeline("text-generation", model="comradepenguin1917/backward-llama2-lora")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("comradepenguin1917/backward-llama2-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use comradepenguin1917/backward-llama2-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "comradepenguin1917/backward-llama2-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "comradepenguin1917/backward-llama2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/comradepenguin1917/backward-llama2-lora

SGLang

How to use comradepenguin1917/backward-llama2-lora 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 "comradepenguin1917/backward-llama2-lora" \
    --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": "comradepenguin1917/backward-llama2-lora",
		"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 "comradepenguin1917/backward-llama2-lora" \
        --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": "comradepenguin1917/backward-llama2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use comradepenguin1917/backward-llama2-lora with Docker Model Runner:
```
docker model run hf.co/comradepenguin1917/backward-llama2-lora
```

Model Card for Model ID

---
language: en
license: mit
base_model: NousResearch/Llama-2-7b-chat-hf
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- peft
- causal-lm
---

# Backward model p(x|y) — LoRA adapter

This repository contains a LoRA adapter trained to predict the original instruction (x) given an output/response (y). The model was finetuned on the `timdettmers/openassistant-guanaco` dataset. The adapter is intended to be loaded on top of the `NousResearch/Llama-2-7b-chat-hf` base model.

## Quick usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base = "NousResearch/Llama-2-7b-chat-hf"
adapter_repo = "."  # local folder or hf repo id

tokenizer = AutoTokenizer.from_pretrained(base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter_repo, device_map="auto")

prompt = "### Output:\nLeaves fall and wind sings.\n\n### Instruction:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Model details

Base model: NousResearch/Llama-2-7b-chat-hf (used as accessible substitute for gated meta-llama/Llama-2-7b-chat-hf).
Fine-tuned task: backward model p(x|y) — predict instruction given output.
Training dataset: timdettmers/openassistant-guanaco (openassistant-guanaco).

LoRA hyperparameters (training)

r: 8
alpha: 16
dropout: 0.05
task_type: CAUSAL_LM

Files included

adapter_config.json — PEFT adapter config
adapter_model.safetensors — LoRA adapter weights
checkpoint-250/, checkpoint-500/ — intermediate adapter checkpoints and trainer state
training_args.bin — HF Trainer training args
README.md — this file

How to reproduce

Run train_backward.py in this repository. Example training command used for experiments (adjust per GPU resources):

python train_backward.py \
    --model_name NousResearch/Llama-2-7b-chat-hf \
    --load_in_4bit \
    --num_train_epochs 2 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 8 \
    --max_train_samples 5000 \
    --output_dir ./backward-finetuned

License

This example uses mit as a placeholder. Replace with the appropriate license.

Contact

Please contact the author for questions about reproduction or use.

Framework versions

PEFT 0.18.0

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