Text Generation
Transformers
Safetensors
RWKV
English
Chinese
Japanese
rwkv07i_moe
rwkv7
hybrid
linear-attention
distillation
tica
Mixture of Experts
conversational
custom_code
Instructions to use OpenMOSE/RWKV-GLM-4.7-Flash-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSE/RWKV-GLM-4.7-Flash-exp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenMOSE/RWKV-GLM-4.7-Flash-exp", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OpenMOSE/RWKV-GLM-4.7-Flash-exp", trust_remote_code=True, device_map="auto") - RWKV
How to use OpenMOSE/RWKV-GLM-4.7-Flash-exp with RWKV:
# 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 Settings
- vLLM
How to use OpenMOSE/RWKV-GLM-4.7-Flash-exp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenMOSE/RWKV-GLM-4.7-Flash-exp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSE/RWKV-GLM-4.7-Flash-exp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenMOSE/RWKV-GLM-4.7-Flash-exp
- SGLang
How to use OpenMOSE/RWKV-GLM-4.7-Flash-exp 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 "OpenMOSE/RWKV-GLM-4.7-Flash-exp" \ --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": "OpenMOSE/RWKV-GLM-4.7-Flash-exp", "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 "OpenMOSE/RWKV-GLM-4.7-Flash-exp" \ --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": "OpenMOSE/RWKV-GLM-4.7-Flash-exp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenMOSE/RWKV-GLM-4.7-Flash-exp with Docker Model Runner:
docker model run hf.co/OpenMOSE/RWKV-GLM-4.7-Flash-exp
Upload folder using huggingface_hub
Browse files- bench.txt +27 -0
- config.json +3 -3
- model.safetensors.index.json +113 -41
bench.txt
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hf (pretrained=/workspace/output/glm4_7_30b/hf_temp_07i,trust_remote_code=True), gen_kwargs: (max_gen_toks=10), limit: None, num_fewshot: None, batch_size: 8
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hf (pretrained=/workspace/output/glm4_7_30b/hf_temp_07i,trust_remote_code=True), gen_kwargs: (max_gen_toks=10), limit: None, num_fewshot: None, batch_size: 8
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hf (pretrained=/workspace/output/glm4_7_30b/hf_temp_07i,trust_remote_code=True), gen_kwargs: (max_gen_toks=64), limit: None, num_fewshot: None, batch_size: 8
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config.json
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"transformer_layers": [-1],
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"rwkv_layers": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],
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"tiny_attention_layers": [
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"rwkv_architecture": "hxa07i",
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"tiny_head_dim": 128,
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"lora_rank_value_residual_mix":-1,
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"lora_rank_key_residual_mix":-1,
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"lora_rank_gate":384,
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"transformer_layers": [-1],
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"rwkv_layers": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],
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"tiny_attention_layers": [0,7,12,16,21,25,29,32,35,36,37,38,39,40,41,42,43,44,45,46],
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"rwkv_architecture": "hxa07i",
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"enable_qk_norm": true,
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"nope_in_transformer": true,
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"tiny_head_dim": 128,
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"tiny_n_heads": 4,
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"tiny_kv_heads": 2,
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"lora_rank_decay": 384,
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"lora_rank_iclr":128,
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"lora_rank_value_residual_mix":-1,
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"lora_rank_key_residual_mix":-1,
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"lora_rank_gate":384,
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model.safetensors.index.json
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"model.layers.0.self_attn.w0": "model-00001-of-00016.safetensors",
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