Instructions to use sasa2000/glm-4-1v-9b-thinking-text-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sasa2000/glm-4-1v-9b-thinking-text-only with Transformers:

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

pipe = pipeline("text-generation", model="sasa2000/glm-4-1v-9b-thinking-text-only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sasa2000/glm-4-1v-9b-thinking-text-only")
model = AutoModelForCausalLM.from_pretrained("sasa2000/glm-4-1v-9b-thinking-text-only")
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 sasa2000/glm-4-1v-9b-thinking-text-only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sasa2000/glm-4-1v-9b-thinking-text-only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sasa2000/glm-4-1v-9b-thinking-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sasa2000/glm-4-1v-9b-thinking-text-only

SGLang

How to use sasa2000/glm-4-1v-9b-thinking-text-only 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 "sasa2000/glm-4-1v-9b-thinking-text-only" \
    --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": "sasa2000/glm-4-1v-9b-thinking-text-only",
		"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 "sasa2000/glm-4-1v-9b-thinking-text-only" \
        --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": "sasa2000/glm-4-1v-9b-thinking-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sasa2000/glm-4-1v-9b-thinking-text-only with Docker Model Runner:
```
docker model run hf.co/sasa2000/glm-4-1v-9b-thinking-text-only
```

GLM-4.1V-9B-Thinking Text-Only

This is a text-only extraction of zai-org/GLM-4.1V-9B-Thinking. It keeps the language model weights and tokenizer assets, and removes the vision/multimodal components.

This repository is not an official ZhipuAI/zai-org release.

Conversion

Source model: zai-org/GLM-4.1V-9B-Thinking
Target model type: glm4
Target architecture: Glm4ForCausalLM
Kept tensors: 523
Dropped tensors: 181
Weight format: sharded safetensors
Weight files: 4 shards at repository root

The original VLM config uses a multimodal GLM4V architecture. For text-only loading with Transformers AutoModelForCausalLM, the language backbone was converted to a loadable glm4 CausalLM config.

Validation

Validated locally with Transformers:

AutoConfig.from_pretrained(...) loads as Glm4Config
AutoTokenizer.from_pretrained(...) loads successfully
AutoModelForCausalLM.from_pretrained(..., torch_dtype="auto", low_cpu_mem_usage=True) loads as Glm4ForCausalLM
A tiny forward pass succeeds and returns logits with shape (1, 1, 151552)
No vision/projector/multimodal tensor names remain in the sharded weight index

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sasa2000/glm-4-1v-9b-thinking-text-only"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

inputs = tokenizer("Explain why the sky looks blue.", return_tensors="pt").to(model.device)
with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=128)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Limitations

This checkpoint is text-only. Image and video inputs are not supported because the vision encoder, multimodal projector, and related preprocessing assets were removed.

Please review the upstream model card and license for the original model's intended use, limitations, and terms.

Downloads last month: 35

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for sasa2000/glm-4-1v-9b-thinking-text-only

Base model

zai-org/GLM-4-9B-0414

Finetuned

zai-org/GLM-4.1V-9B-Thinking

Finetuned

(12)

this model