Text Generation
Transformers
Safetensors
glm4_moe
compression
expert-merging
Mixture of Experts
code
conversational
Instructions to use bknyaz/GLM-4.5-Air-REAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bknyaz/GLM-4.5-Air-REAM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bknyaz/GLM-4.5-Air-REAM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("bknyaz/GLM-4.5-Air-REAM") model = AutoModelForMultimodalLM.from_pretrained("bknyaz/GLM-4.5-Air-REAM") 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 bknyaz/GLM-4.5-Air-REAM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bknyaz/GLM-4.5-Air-REAM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bknyaz/GLM-4.5-Air-REAM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bknyaz/GLM-4.5-Air-REAM
- SGLang
How to use bknyaz/GLM-4.5-Air-REAM 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 "bknyaz/GLM-4.5-Air-REAM" \ --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": "bknyaz/GLM-4.5-Air-REAM", "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 "bknyaz/GLM-4.5-Air-REAM" \ --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": "bknyaz/GLM-4.5-Air-REAM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bknyaz/GLM-4.5-Air-REAM with Docker Model Runner:
docker model run hf.co/bknyaz/GLM-4.5-Air-REAM
Upload Glm4MoeForCausalLM
Browse files- README.md +199 -0
- config.json +59 -0
- generation_config.json +10 -0
- model-00001-of-00033.safetensors +3 -0
- model-00002-of-00033.safetensors +3 -0
- model-00003-of-00033.safetensors +3 -0
- model-00004-of-00033.safetensors +3 -0
- model-00005-of-00033.safetensors +3 -0
- model-00006-of-00033.safetensors +3 -0
- model-00007-of-00033.safetensors +3 -0
- model-00008-of-00033.safetensors +3 -0
- model-00009-of-00033.safetensors +3 -0
- model-00010-of-00033.safetensors +3 -0
- model-00011-of-00033.safetensors +3 -0
- model-00012-of-00033.safetensors +3 -0
- model-00013-of-00033.safetensors +3 -0
- model-00014-of-00033.safetensors +3 -0
- model-00015-of-00033.safetensors +3 -0
- model-00016-of-00033.safetensors +3 -0
- model-00017-of-00033.safetensors +3 -0
- model-00018-of-00033.safetensors +3 -0
- model-00019-of-00033.safetensors +3 -0
- model-00020-of-00033.safetensors +3 -0
- model-00021-of-00033.safetensors +3 -0
- model-00022-of-00033.safetensors +3 -0
- model-00023-of-00033.safetensors +3 -0
- model-00024-of-00033.safetensors +3 -0
- model-00025-of-00033.safetensors +3 -0
- model-00026-of-00033.safetensors +3 -0
- model-00027-of-00033.safetensors +3 -0
- model-00028-of-00033.safetensors +3 -0
- model-00029-of-00033.safetensors +3 -0
- model-00030-of-00033.safetensors +3 -0
- model-00031-of-00033.safetensors +3 -0
- model-00032-of-00033.safetensors +3 -0
- model-00033-of-00033.safetensors +3 -0
- model.safetensors.index.json +0 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
ADDED
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{
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"architectures": [
|
| 3 |
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"Glm4MoeForCausalLM"
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| 4 |
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],
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| 5 |
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"attention_bias": true,
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": [
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151329,
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],
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"first_k_dense_replace": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 10944,
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"max_position_embeddings": 131072,
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| 20 |
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"merge_args": {
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| 21 |
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"balance_group_size": 16,
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| 22 |
+
"datamix_ratio": "0,0.3,0.7",
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