Instructions to use hf-internal-testing/tiny-random-GlmForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-GlmForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-GlmForCausalLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GlmForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-GlmForCausalLM")
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 hf-internal-testing/tiny-random-GlmForCausalLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-GlmForCausalLM

SGLang

How to use hf-internal-testing/tiny-random-GlmForCausalLM 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 "hf-internal-testing/tiny-random-GlmForCausalLM" \
    --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": "hf-internal-testing/tiny-random-GlmForCausalLM",
		"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 "hf-internal-testing/tiny-random-GlmForCausalLM" \
        --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": "hf-internal-testing/tiny-random-GlmForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-GlmForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-GlmForCausalLM
```

tiny-random-GlmForCausalLM / tokenizer_config.json

Xenova HF Staff

Upload tokenizer

5f79173 verified over 1 year ago

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	{
	"added_tokens_decoder": {
	"59246": {
	"content": "<\|endoftext\|>",
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	"normalized": false,
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	"single_word": false,
	"special": true
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	"59252": {
	"content": "<\|system\|>",
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	"content": "<\|user\|>",
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	"59254": {
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	"59255": {
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	},
	"59256": {
	"content": "<\|begin_of_image\|>",
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	"special": true
	},
	"59257": {
	"content": "<\|end_of_image\|>",
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	"59258": {
	"content": "<\|begin_of_video\|>",
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	"59259": {
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	},
	"additional_special_tokens": [
	"<\|endoftext\|>",
	"[MASK]",
	"[gMASK]",
	"[sMASK]",
	"<sop>",
	"<eop>",
	"<\|system\|>",
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|observation\|>",
	"<\|begin_of_image\|>",
	"<\|end_of_image\|>",
	"<\|begin_of_video\|>",
	"<\|end_of_video\|>"
	],
	"chat_template": "{% for item in messages %}{% if item['role'] == 'system' %}<\|system\|>\n{{ item['content'] }}{% elif item['role'] == 'user' %}<\|user\|>\n{{ item['content'] }}{% elif item['role'] == 'assistant' %}<\|assistant\|>\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<\|assistant\|>\n{% endif %}",
	"clean_up_tokenization_spaces": false,
	"do_lower_case": false,
	"eos_token": "<\|endoftext\|>",
	"extra_special_tokens": {},
	"image_size": 448,
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 8192,
	"pad_token": "<\|endoftext\|>",
	"padding_side": "left",
	"remove_space": false,
	"tokenizer_class": "PreTrainedTokenizerFast"
	}