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

Libraries

How to use erbacher/zephyr-7b-ikat with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("erbacher/zephyr-7b-ikat")
model = AutoModelForCausalLM.from_pretrained("erbacher/zephyr-7b-ikat")
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

vLLM

How to use erbacher/zephyr-7b-ikat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/erbacher/zephyr-7b-ikat

SGLang

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

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

zephyr-7b-ikat

This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5166

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: 2e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 256
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
0.7437	0.62	17	0.6867
0.6372	1.63	35	0.6215
0.6078	2.64	53	0.5859
0.5724	3.62	70	0.5625
0.5613	4.63	88	0.5448
0.5427	5.64	106	0.5337
0.5388	6.62	123	0.5274
0.5284	7.63	141	0.5229
0.5285	8.64	159	0.5188
0.5222	9.61	176	0.5165

Framework versions

Transformers 4.35.0
Pytorch 2.1.1+cu118
Datasets 2.14.6
Tokenizers 0.14.1

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