Text Generation
Transformers
Safetensors
English
glm4
text-generation-inference
unsloth
conversational
Instructions to use aimeri/spoomplesmaxx-base-glm4-32b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aimeri/spoomplesmaxx-base-glm4-32b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aimeri/spoomplesmaxx-base-glm4-32b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aimeri/spoomplesmaxx-base-glm4-32b") model = AutoModelForCausalLM.from_pretrained("aimeri/spoomplesmaxx-base-glm4-32b", 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 aimeri/spoomplesmaxx-base-glm4-32b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aimeri/spoomplesmaxx-base-glm4-32b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aimeri/spoomplesmaxx-base-glm4-32b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aimeri/spoomplesmaxx-base-glm4-32b
- SGLang
How to use aimeri/spoomplesmaxx-base-glm4-32b 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 "aimeri/spoomplesmaxx-base-glm4-32b" \ --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": "aimeri/spoomplesmaxx-base-glm4-32b", "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 "aimeri/spoomplesmaxx-base-glm4-32b" \ --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": "aimeri/spoomplesmaxx-base-glm4-32b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use aimeri/spoomplesmaxx-base-glm4-32b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aimeri/spoomplesmaxx-base-glm4-32b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aimeri/spoomplesmaxx-base-glm4-32b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aimeri/spoomplesmaxx-base-glm4-32b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="aimeri/spoomplesmaxx-base-glm4-32b", max_seq_length=2048, ) - Docker Model Runner
How to use aimeri/spoomplesmaxx-base-glm4-32b with Docker Model Runner:
docker model run hf.co/aimeri/spoomplesmaxx-base-glm4-32b
(Trained with Unsloth)
Browse files- .gitattributes +1 -0
- config.json +28 -0
- special_tokens_map.json +26 -0
- tokenizer.json +3 -0
- tokenizer_config.json +153 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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config.json
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{
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"architectures": [
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"Glm4ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"torch_dtype": "bfloat16",
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"eos_token_id": 151329,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 6144,
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"initializer_range": 0.02,
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"intermediate_size": 23040,
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"max_position_embeddings": 32768,
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"model_name": "zai-org/GLM-4-32B-Base-0414",
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"model_type": "glm4",
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"num_attention_heads": 48,
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"num_hidden_layers": 61,
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"num_key_value_heads": 2,
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"pad_token_id": 151343,
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"partial_rotary_factor": 0.5,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"unsloth_version": "2026.3.3",
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"use_cache": true,
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"vocab_size": 151552
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}
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"[MASK]",
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"[gMASK]",
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"[sMASK]",
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"<sop>",
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"<eop>",
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"<|system|>",
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"<|user|>",
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"<|assistant|>",
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"<|observation|>",
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"<|begin_of_image|>",
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"<|end_of_image|>",
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"<|begin_of_video|>",
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"<|end_of_video|>"
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],
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"pad_token": "<|PAD_TOKEN|>"
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:8ed7fb7706cfed63034a5ab78e7988c7bb1489819ee5c8dc7090ce2b3c3c7427
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size 19966686
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tokenizer_config.json
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},
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|
| 125 |
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"<|endoftext|>",
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| 126 |
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"[MASK]",
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| 127 |
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"[gMASK]",
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| 128 |
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"[sMASK]",
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| 129 |
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"<sop>",
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"<eop>",
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"<|system|>",
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| 132 |
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"<|user|>",
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"<|assistant|>",
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| 134 |
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"<|observation|>",
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| 135 |
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"<|begin_of_image|>",
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| 136 |
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"<|end_of_image|>",
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| 137 |
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"<|begin_of_video|>",
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| 138 |
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"<|end_of_video|>"
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| 139 |
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],
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| 140 |
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"clean_up_tokenization_spaces": false,
|
| 141 |
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"do_lower_case": false,
|
| 142 |
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"eos_token": "<|endoftext|>",
|
| 143 |
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"extra_special_tokens": {},
|
| 144 |
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"model_input_names": [
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| 145 |
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"input_ids",
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| 146 |
+
"attention_mask"
|
| 147 |
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],
|
| 148 |
+
"model_max_length": 128000,
|
| 149 |
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"pad_token": "<|PAD_TOKEN|>",
|
| 150 |
+
"padding_side": "left",
|
| 151 |
+
"remove_space": false,
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| 152 |
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"tokenizer_class": "PreTrainedTokenizerFast"
|
| 153 |
+
}
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