Instructions to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM with Transformers:

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

pipe = pipeline("text-generation", model="RESMP-DEV/GLM-4.7-Flash-Trellis-MM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RESMP-DEV/GLM-4.7-Flash-Trellis-MM")
model = AutoModelForCausalLM.from_pretrained("RESMP-DEV/GLM-4.7-Flash-Trellis-MM")

Trellis

How to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RESMP-DEV/GLM-4.7-Flash-Trellis-MM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RESMP-DEV/GLM-4.7-Flash-Trellis-MM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RESMP-DEV/GLM-4.7-Flash-Trellis-MM

SGLang

How to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM 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 "RESMP-DEV/GLM-4.7-Flash-Trellis-MM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RESMP-DEV/GLM-4.7-Flash-Trellis-MM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "RESMP-DEV/GLM-4.7-Flash-Trellis-MM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RESMP-DEV/GLM-4.7-Flash-Trellis-MM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RESMP-DEV/GLM-4.7-Flash-Trellis-MM with Docker Model Runner:
```
docker model run hf.co/RESMP-DEV/GLM-4.7-Flash-Trellis-MM
```

Kearm commited on Feb 5

Commit

7162e09

verified ·

1 Parent(s): 9d8e422

Update README.md

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@@ -5,7 +5,8 @@ language:
 library_name: transformers
 license: mit
 pipeline_tag: text-generation
-base_model: zai-org/GLM-4.7-Flash
 tags:
   - trellis
   - quantized
@@ -14,7 +15,7 @@ tags:
   - mixed-precision
   - cuda
   - glm
-quantized_by: Metal Marlin
 ---
 # GLM-4.7-Flash-Trellis-3.8bpw

 library_name: transformers
 license: mit
 pipeline_tag: text-generation
+base_model:
+  - zai-org/GLM-4.7-Flash
 tags:
   - trellis
   - quantized
   - mixed-precision
   - cuda
   - glm
+  - metal-marlin
 ---
 # GLM-4.7-Flash-Trellis-3.8bpw