Instructions to use areddydev/xlam-1b-dpo1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use areddydev/xlam-1b-dpo1 with Transformers:

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

pipe = pipeline("text-generation", model="areddydev/xlam-1b-dpo1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("areddydev/xlam-1b-dpo1")
model = AutoModelForCausalLM.from_pretrained("areddydev/xlam-1b-dpo1")
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 areddydev/xlam-1b-dpo1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "areddydev/xlam-1b-dpo1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "areddydev/xlam-1b-dpo1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/areddydev/xlam-1b-dpo1

SGLang

How to use areddydev/xlam-1b-dpo1 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 "areddydev/xlam-1b-dpo1" \
    --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": "areddydev/xlam-1b-dpo1",
		"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 "areddydev/xlam-1b-dpo1" \
        --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": "areddydev/xlam-1b-dpo1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use areddydev/xlam-1b-dpo1 with Docker Model Runner:
```
docker model run hf.co/areddydev/xlam-1b-dpo1
```

xlam-1b-dpo1 / tokenizer_config.json

areddydev

Upload tokenizer

72abe50 verified about 1 year ago

raw

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5.18 kB

	{
	"add_bos_token": true,
	"add_eos_token": false,
	"add_prefix_space": null,
	"added_tokens_decoder": {
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	"32019": {
	"content": "<\|User\|>",
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	"32020": {
	"content": "<\|Assistant\|>",
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	},
	"32021": {
	"content": "<\|EOT\|>",
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	}
	},
	"additional_special_tokens": [
	"<\|EOT\|>"
	],
	"bos_token": "<｜begin▁of▁sentence｜>",
	"chat_template": "{% set system_message = 'You are an AI assistant for function calling.For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '### Instruction:\\n' + content + '\\n### Response:' }}{% elif message['role'] == 'assistant' %}{{ '\\n' + content + '\\n<\|EOT\|>\\n' }}{% endif %}{% endfor %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|EOT\|>",
	"extra_special_tokens": {},
	"legacy": true,
	"max_length": 16384,
	"model_max_length": 16384,
	"pad_token": "<｜end▁of▁sentence｜>",
	"padding_side": "right",
	"sp_model_kwargs": {},
	"split_special_tokens": false,
	"stride": 0,
	"tokenizer_class": "LlamaTokenizerFast",
	"truncation_side": "right",
	"truncation_strategy": "longest_first",
	"unk_token": null,
	"use_default_system_prompt": false
	}