Instructions to use DatPySci/zephyr-7b-kto-iter0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DatPySci/zephyr-7b-kto-iter0 with Transformers:

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

pipe = pipeline("text-generation", model="DatPySci/zephyr-7b-kto-iter0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DatPySci/zephyr-7b-kto-iter0")
model = AutoModelForCausalLM.from_pretrained("DatPySci/zephyr-7b-kto-iter0")
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 DatPySci/zephyr-7b-kto-iter0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DatPySci/zephyr-7b-kto-iter0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DatPySci/zephyr-7b-kto-iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DatPySci/zephyr-7b-kto-iter0

SGLang

How to use DatPySci/zephyr-7b-kto-iter0 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 "DatPySci/zephyr-7b-kto-iter0" \
    --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": "DatPySci/zephyr-7b-kto-iter0",
		"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 "DatPySci/zephyr-7b-kto-iter0" \
        --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": "DatPySci/zephyr-7b-kto-iter0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DatPySci/zephyr-7b-kto-iter0 with Docker Model Runner:
```
docker model run hf.co/DatPySci/zephyr-7b-kto-iter0
```

DatPySci commited on Apr 9, 2024

Commit

a6b0cfc

verified ·

1 Parent(s): 6576f06

Training in progress, step 100

Browse files

Files changed (3) hide show

config.json +1 -1
tokenizer_config.json +1 -1
training_args.bin +1 -1

config.json CHANGED Viewed

@@ -21,6 +21,6 @@
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.39.3",
-  "use_cache": true,
   "vocab_size": 32000
 }

   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.39.3",
+  "use_cache": false,
   "vocab_size": 32000
 }

tokenizer_config.json CHANGED Viewed

@@ -39,5 +39,5 @@
   "spaces_between_special_tokens": false,
   "tokenizer_class": "LlamaTokenizer",
   "unk_token": "<unk>",
-  "use_default_system_prompt": false
 }

   "spaces_between_special_tokens": false,
   "tokenizer_class": "LlamaTokenizer",
   "unk_token": "<unk>",
+  "use_default_system_prompt": true
 }

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
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