Instructions to use Delta-Vector/GLM-4-32B-Tulu-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/GLM-4-32B-Tulu-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/GLM-4-32B-Tulu-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/GLM-4-32B-Tulu-Instruct")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/GLM-4-32B-Tulu-Instruct", device_map="auto")
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 Delta-Vector/GLM-4-32B-Tulu-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/GLM-4-32B-Tulu-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/GLM-4-32B-Tulu-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/GLM-4-32B-Tulu-Instruct

SGLang

How to use Delta-Vector/GLM-4-32B-Tulu-Instruct 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 "Delta-Vector/GLM-4-32B-Tulu-Instruct" \
    --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": "Delta-Vector/GLM-4-32B-Tulu-Instruct",
		"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 "Delta-Vector/GLM-4-32B-Tulu-Instruct" \
        --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": "Delta-Vector/GLM-4-32B-Tulu-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/GLM-4-32B-Tulu-Instruct with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/GLM-4-32B-Tulu-Instruct
```

GLM-4-32B-Tulu-Instruct / README.md

Delta-Vector

Update README.md

f7778ff verified about 1 year ago

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	datasets:
	- Delta-Vector/Hydrus-Preview-Tulu-3-SFT-Mix
	base_model:
	- arcee-ai/GLM-4-32B-Base-32K
	library_name: transformers
	tags:
	- instruct
	- code
	- chemistry
	- GLM
	---

	<div align="center">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/j9LpDp1Wup-m-IJ_XMh23.png" alt="Image" width="400" />
	<br>
	<small><em>Promise I will never go blonde like Kanye</em></small>
	</div>

	---
	# Overview

	Didn't really have any cool README ideas for this so we're just going with just whatever song i'm listening to rn and it happened to be `Baby i'm bleeding`

	Nevertheless, This is a finetune from the 32K context extended (or fixed?) Arcee GLM4 base - Trained shrimply with just the Tulu-SFT-Mixture but I removed Safety alignment examples. Came out pretty well, It uses chatML due to the GLM4 Format giving me a headache. It's a decently competant assistant although I haven't done any testing on how well the model performs at longer-contexts, nor have i done any RL afterwards to fix up it's edges.

	Think it should be a decent base for any future finetunes, I felt that GLM4 really wasn't given the proper time of day and it's a way better base then any Qwen3 model.

	# Quants

	GGUF: https://huggingface.co/mradermacher/GLM-Tulu-ChatML-GGUF

	Imatrix GGUF: https://huggingface.co/mradermacher/GLM-Tulu-ChatML-i1-GGUF

	# Prompting

	The model was trained with ChatML formatting

	```
	"""<\|im_start\|>system
	system prompt<\|im_end\|>
	<\|im_start\|>user
	Hi there!<\|im_end\|>
	<\|im_start\|>assistant
	Nice to meet you!<\|im_end\|>
	<\|im_start\|>user
	Can I ask a question?<\|im_end\|>
	<\|im_start\|>assistant
	"""
	```

	# Configs

	WandB : https://wandb.ai/new-eden/Training-A100/runs/05kktve8?nw=nwuserdeltavector

	This train took 15 hours on 8xB200s provided by Deepinfra and Cognitive Computations, Config is linked in the WandB

	# Credits

	Thank you to Lucy, Auri, NyxKrage, Creators of the Tulu-SFT-Mix and everyone at Anthracite & Allura