Instructions to use taicheng/zephyr-7b-align-scan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taicheng/zephyr-7b-align-scan with Transformers:

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

pipe = pipeline("text-generation", model="taicheng/zephyr-7b-align-scan")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taicheng/zephyr-7b-align-scan")
model = AutoModelForCausalLM.from_pretrained("taicheng/zephyr-7b-align-scan")
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 taicheng/zephyr-7b-align-scan with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/taicheng/zephyr-7b-align-scan

SGLang

How to use taicheng/zephyr-7b-align-scan 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 "taicheng/zephyr-7b-align-scan" \
    --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": "taicheng/zephyr-7b-align-scan",
		"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 "taicheng/zephyr-7b-align-scan" \
        --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": "taicheng/zephyr-7b-align-scan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taicheng/zephyr-7b-align-scan with Docker Model Runner:
```
docker model run hf.co/taicheng/zephyr-7b-align-scan
```

zephyr-7b-align-scan

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5960
Rewards/chosen: -0.6252
Rewards/rejected: -1.0907
Rewards/accuracies: 0.3690
Rewards/margins: 0.4655
Logps/rejected: -190.1945
Logps/chosen: -137.0085
Logits/rejected: 0.9319
Logits/chosen: 0.4522

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.6719	0.1047	100	-2.3487	-2.3310	-73.2865	-85.4479	0.6687	0.3274	0.0120	0.0552	-0.0432
0.6488	0.2093	200	-1.2584	-1.2396	-102.5743	-130.3725	0.6348	0.3373	-0.2808	0.2116	-0.4924
0.6331	0.3140	300	-1.1873	-1.0320	-120.1307	-157.0977	0.6195	0.3452	-0.4564	0.3033	-0.7597
0.6321	0.4186	400	0.0335	0.3728	-146.9637	-190.2757	0.6099	0.3631	-0.7247	0.3667	-1.0915
0.6318	0.5233	500	2.6547	2.9545	-155.4930	-204.6371	0.6105	0.3552	-0.8100	0.4251	-1.2351
0.5978	0.6279	600	0.9606	1.4420	-147.8560	-199.5121	0.6015	0.3591	-0.7336	0.4502	-1.1838
0.6113	0.7326	700	1.1833	1.7188	-150.6854	-204.9195	0.5986	0.3651	-0.7619	0.4760	-1.2379
0.5885	0.8373	800	0.5613	1.0128	-141.6925	-192.4845	0.5974	0.3690	-0.6720	0.4415	-1.1136
0.595	0.9419	900	0.4326	0.9106	-136.2882	-189.5506	0.5958	0.3710	-0.6180	0.4663	-1.0842

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for taicheng/zephyr-7b-align-scan

Base model

mistralai/Mistral-7B-v0.1

Finetuned

alignment-handbook/zephyr-7b-sft-full

Finetuned

(393)

this model

taicheng
/

zephyr-7b-align-scan

zephyr-7b-align-scan

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for taicheng/zephyr-7b-align-scan

Dataset used to train taicheng/zephyr-7b-align-scan