Instructions to use wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew with Transformers:

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

pipe = pipeline("text-generation", model="wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew")
model = AutoModelForCausalLM.from_pretrained("wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew", 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 wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew

SGLang

How to use wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew 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 "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew" \
    --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": "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew",
		"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 "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew" \
        --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": "wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew with Docker Model Runner:
```
docker model run hf.co/wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew
```

Llama-2-7B SSFT-CB + ARC-Challenge WSR-LoRA

This is a fully merged WSR-LoRA checkpoint trained on the ARC-Challenge training split from wvnvwn/llama2-7b-chat-lr5e-5-ssft-cb.

Training used learning rate 2e-4, 3 epochs, physical/effective batch size 16, cosine scheduling with warmup ratio 0.1, BF16, maximum sequence length 1,024, and seed 42. WSR-LoRA used rank 16, alpha 16, dropout 0, and target modules q_proj, k_proj, v_proj, up_proj, and down_proj. Rotation reused a 160-layer safety SVD basis constructed from all 4,994 Circuit Breaker examples. Factor importance used 512 safety examples, with the top 10% of entries frozen in both A and B factors.

The weights are already merged into the starting model. See wsrlora_run_config.json for the full run provenance.

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for wvnvwn/llama2-7b-chat-lr5e-5-arcc-lr2e-4-cbwsrnew

Base model

wvnvwn/llama2-7b-chat-lr5e-5-ssft-cb

Finetuned

(6)

this model