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
```

Delta-Vector commited on Jul 11, 2025

Commit

7434438

verified ·

1 Parent(s): eca4900

Create README.md

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README.md +65 -0

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	@@ -0,0 +1,65 @@

+---
+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(and other formats like it, Such as Dan-chat) 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