Instructions to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="trl-internal-testing/tiny-Glm5NextForConditionalGeneration") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("trl-internal-testing/tiny-Glm5NextForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("trl-internal-testing/tiny-Glm5NextForConditionalGeneration", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-Glm5NextForConditionalGeneration" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-Glm5NextForConditionalGeneration", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-Glm5NextForConditionalGeneration
- SGLang
How to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration 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 "trl-internal-testing/tiny-Glm5NextForConditionalGeneration" \ --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": "trl-internal-testing/tiny-Glm5NextForConditionalGeneration", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "trl-internal-testing/tiny-Glm5NextForConditionalGeneration" \ --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": "trl-internal-testing/tiny-Glm5NextForConditionalGeneration", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-Glm5NextForConditionalGeneration
Download config.json from trl-internal-testing/tiny-Glm5NextForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 67.1 kB
-
https://huggingface.co/trl-internal-testing/tiny-Glm5NextForConditionalGeneration/resolve/main/config.json
- Command line
-
hf download hf://trl-internal-testing/tiny-Glm5NextForConditionalGeneration/config.json
-
curl -L -o config.json https://huggingface.co/trl-internal-testing/tiny-Glm5NextForConditionalGeneration/resolve/main/config.json
67.1 kB
| { | |
| "architectures": [ | |
| "Glm5NextForConditionalGeneration" | |
| ], | |
| "dtype": "bfloat16", | |
| "image_end_token_id": 154831, | |
| "image_start_token_id": 154830, | |
| "image_token_id": 154854, | |
| "model_type": "glm5_next", | |
| "quantization_config": { | |
| "activation_scheme": "dynamic", | |
| "dequantize": false, | |
| "modules_to_convert": null, | |
| "modules_to_not_convert": [ | |
| "attn_mha", | |
| "attn_mqa", | |
| "dt_bias", | |
| "hyper_connection", | |
| "lm_head", | |
| "mapping_proj", | |
| "model.embed_tokens", | |
| "model.layers.0.attn_hc.base", | |
| "model.layers.0.attn_hc.fn", | |
| "model.layers.0.attn_hc.scale", | |
| "model.layers.0.ffn_hc.base", | |
| "model.layers.0.ffn_hc.fn", | |
| "model.layers.0.ffn_hc.scale", | |
| "model.layers.0.input_layernorm", | |
| "model.layers.0.post_attention_layernorm", | |
| "model.layers.0.self_attn.forget_gate.A_log", | |
| "model.layers.0.self_attn.b_proj", | |
| "model.layers.0.self_attn.forget_gate.dt_bias", | |
| "model.layers.0.self_attn.f_a_proj", | |
| "model.layers.0.self_attn.f_b_proj", | |
| "model.layers.0.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.0.self_attn.g_a_proj", | |
| "model.layers.0.self_attn.g_b_proj", | |
| "model.layers.0.self_attn.k_conv1d", | |
| "model.layers.0.self_attn.k_proj", | |
| "model.layers.0.self_attn.o_norm", | |
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| "model.layers.0.self_attn.q_conv1d", | |
| "model.layers.0.self_attn.q_proj", | |
| "model.layers.0.self_attn.qkv_proj", | |
| "model.layers.0.self_attn.v_conv1d", | |
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| "model.layers.1.attn_hc.base", | |
| "model.layers.1.attn_hc.fn", | |
| "model.layers.1.attn_hc.scale", | |
| "model.layers.1.ffn_hc.base", | |
| "model.layers.1.ffn_hc.fn", | |
| "model.layers.1.ffn_hc.scale", | |
| "model.layers.1.input_layernorm", | |
| "model.layers.1.post_attention_layernorm", | |
| "model.layers.1.self_attn.forget_gate.A_log", | |
| "model.layers.1.self_attn.b_proj", | |
| "model.layers.1.self_attn.forget_gate.dt_bias", | |
| "model.layers.1.self_attn.f_a_proj", | |
| "model.layers.1.self_attn.f_b_proj", | |
| "model.layers.1.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.1.self_attn.g_a_proj", | |
| "model.layers.1.self_attn.g_b_proj", | |
| "model.layers.1.self_attn.k_conv1d", | |
| "model.layers.1.self_attn.k_proj", | |
| "model.layers.1.self_attn.o_norm", | |
| "model.layers.1.self_attn.o_proj", | |
| "model.layers.1.self_attn.q_conv1d", | |
| "model.layers.1.self_attn.q_proj", | |
| "model.layers.1.self_attn.qkv_proj", | |
| "model.layers.1.self_attn.v_conv1d", | |
| "model.layers.1.self_attn.v_proj", | |
| "model.layers.10.attn_hc.base", | |
| "model.layers.10.attn_hc.fn", | |
| "model.layers.10.attn_hc.scale", | |
| "model.layers.10.ffn_hc.base", | |
| "model.layers.10.ffn_hc.fn", | |
| "model.layers.10.ffn_hc.scale", | |
| "model.layers.10.input_layernorm", | |
| "model.layers.10.mlp.gate", | |
| "model.layers.10.mlp.gate.e_score_correction_bias", | |
| "model.layers.10.post_attention_layernorm", | |
| "model.layers.10.self_attn.forget_gate.A_log", | |
| "model.layers.10.self_attn.b_proj", | |
| "model.layers.10.self_attn.forget_gate.dt_bias", | |
| "model.layers.10.self_attn.f_a_proj", | |
| "model.layers.10.self_attn.f_b_proj", | |
| "model.layers.10.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.10.self_attn.g_a_proj", | |
| "model.layers.10.self_attn.g_b_proj", | |
| "model.layers.10.self_attn.k_conv1d", | |
| "model.layers.10.self_attn.k_proj", | |
| "model.layers.10.self_attn.o_norm", | |
| "model.layers.10.self_attn.o_proj", | |
| "model.layers.10.self_attn.q_conv1d", | |
| "model.layers.10.self_attn.q_proj", | |
| "model.layers.10.self_attn.qkv_proj", | |
| "model.layers.10.self_attn.v_conv1d", | |
| "model.layers.10.self_attn.v_proj", | |
| "model.layers.11.attn_hc.base", | |
| "model.layers.11.attn_hc.fn", | |
| "model.layers.11.attn_hc.scale", | |
| "model.layers.11.ffn_hc.base", | |
| "model.layers.11.ffn_hc.fn", | |
| "model.layers.11.ffn_hc.scale", | |
| "model.layers.11.input_layernorm", | |
| "model.layers.11.mlp.gate", | |
| "model.layers.11.mlp.gate.e_score_correction_bias", | |
| "model.layers.11.post_attention_layernorm", | |
| "model.layers.11.self_attn.indexer.index_kpool_compress_ape", | |
| "model.layers.11.self_attn.indexer.index_kpool_compress_gate", | |
| "model.layers.11.self_attn.indexer.k_norm", | |
| "model.layers.11.self_attn.indexer.k_norm.bias", | |
| "model.layers.11.self_attn.indexer.weights_proj", | |
| "model.layers.11.self_attn.indexer.wk", | |
| "model.layers.11.self_attn.indexer.wq_b", | |
| "model.layers.11.self_attn.kv_a_layernorm", | |
| "model.layers.11.self_attn.kv_b_proj", | |
| "model.layers.11.self_attn.q_a_layernorm", | |
| "model.layers.12.attn_hc.base", | |
| "model.layers.12.attn_hc.fn", | |
| "model.layers.12.attn_hc.scale", | |
| "model.layers.12.ffn_hc.base", | |
| "model.layers.12.ffn_hc.fn", | |
| "model.layers.12.ffn_hc.scale", | |
| "model.layers.12.input_layernorm", | |
| "model.layers.12.mlp.gate", | |
| "model.layers.12.mlp.gate.e_score_correction_bias", | |
| "model.layers.12.post_attention_layernorm", | |
| "model.layers.12.self_attn.forget_gate.A_log", | |
| "model.layers.12.self_attn.b_proj", | |
| "model.layers.12.self_attn.forget_gate.dt_bias", | |
| "model.layers.12.self_attn.f_a_proj", | |
| "model.layers.12.self_attn.f_b_proj", | |
| "model.layers.12.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.12.self_attn.g_a_proj", | |
| "model.layers.12.self_attn.g_b_proj", | |
| "model.layers.12.self_attn.k_conv1d", | |
| "model.layers.12.self_attn.k_proj", | |
| "model.layers.12.self_attn.o_norm", | |
| "model.layers.12.self_attn.o_proj", | |
| "model.layers.12.self_attn.q_conv1d", | |
| "model.layers.12.self_attn.q_proj", | |
| "model.layers.12.self_attn.qkv_proj", | |
| "model.layers.12.self_attn.v_conv1d", | |
| "model.layers.12.self_attn.v_proj", | |
| "model.layers.13.attn_hc.base", | |
| "model.layers.13.attn_hc.fn", | |
| "model.layers.13.attn_hc.scale", | |
| "model.layers.13.ffn_hc.base", | |
| "model.layers.13.ffn_hc.fn", | |
| "model.layers.13.ffn_hc.scale", | |
| "model.layers.13.input_layernorm", | |
| "model.layers.13.mlp.gate", | |
| "model.layers.13.mlp.gate.e_score_correction_bias", | |
| "model.layers.13.post_attention_layernorm", | |
| "model.layers.13.self_attn.forget_gate.A_log", | |
| "model.layers.13.self_attn.b_proj", | |
| "model.layers.13.self_attn.forget_gate.dt_bias", | |
| "model.layers.13.self_attn.f_a_proj", | |
| "model.layers.13.self_attn.f_b_proj", | |
| "model.layers.13.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.13.self_attn.g_a_proj", | |
| "model.layers.13.self_attn.g_b_proj", | |
| "model.layers.13.self_attn.k_conv1d", | |
| "model.layers.13.self_attn.k_proj", | |
| "model.layers.13.self_attn.o_norm", | |
| "model.layers.13.self_attn.o_proj", | |
| "model.layers.13.self_attn.q_conv1d", | |
| "model.layers.13.self_attn.q_proj", | |
| "model.layers.13.self_attn.qkv_proj", | |
| "model.layers.13.self_attn.v_conv1d", | |
| "model.layers.13.self_attn.v_proj", | |
| "model.layers.14.attn_hc.base", | |
| "model.layers.14.attn_hc.fn", | |
| "model.layers.14.attn_hc.scale", | |
| "model.layers.14.ffn_hc.base", | |
| "model.layers.14.ffn_hc.fn", | |
| "model.layers.14.ffn_hc.scale", | |
| "model.layers.14.input_layernorm", | |
| "model.layers.14.mlp.gate", | |
| "model.layers.14.mlp.gate.e_score_correction_bias", | |
| "model.layers.14.post_attention_layernorm", | |
| "model.layers.14.self_attn.forget_gate.A_log", | |
| "model.layers.14.self_attn.b_proj", | |
| "model.layers.14.self_attn.forget_gate.dt_bias", | |
| "model.layers.14.self_attn.f_a_proj", | |
| "model.layers.14.self_attn.f_b_proj", | |
| "model.layers.14.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.14.self_attn.g_a_proj", | |
| "model.layers.14.self_attn.g_b_proj", | |
| "model.layers.14.self_attn.k_conv1d", | |
| "model.layers.14.self_attn.k_proj", | |
| "model.layers.14.self_attn.o_norm", | |
| "model.layers.14.self_attn.o_proj", | |
| "model.layers.14.self_attn.q_conv1d", | |
| "model.layers.14.self_attn.q_proj", | |
| "model.layers.14.self_attn.qkv_proj", | |
| "model.layers.14.self_attn.v_conv1d", | |
| "model.layers.14.self_attn.v_proj", | |
| "model.layers.15.attn_hc.base", | |
| "model.layers.15.attn_hc.fn", | |
| "model.layers.15.attn_hc.scale", | |
| "model.layers.15.ffn_hc.base", | |
| "model.layers.15.ffn_hc.fn", | |
| "model.layers.15.ffn_hc.scale", | |
| "model.layers.15.input_layernorm", | |
| "model.layers.15.mlp.gate", | |
| "model.layers.15.mlp.gate.e_score_correction_bias", | |
| "model.layers.15.post_attention_layernorm", | |
| "model.layers.15.self_attn.indexer.index_kpool_compress_ape", | |
| "model.layers.15.self_attn.indexer.index_kpool_compress_gate", | |
| "model.layers.15.self_attn.indexer.k_norm", | |
| "model.layers.15.self_attn.indexer.k_norm.bias", | |
| "model.layers.15.self_attn.indexer.weights_proj", | |
| "model.layers.15.self_attn.indexer.wk", | |
| "model.layers.15.self_attn.indexer.wq_b", | |
| "model.layers.15.self_attn.kv_a_layernorm", | |
| "model.layers.15.self_attn.kv_b_proj", | |
| "model.layers.15.self_attn.q_a_layernorm", | |
| "model.layers.16.attn_hc.base", | |
| "model.layers.16.attn_hc.fn", | |
| "model.layers.16.attn_hc.scale", | |
| "model.layers.16.ffn_hc.base", | |
| "model.layers.16.ffn_hc.fn", | |
| "model.layers.16.ffn_hc.scale", | |
| "model.layers.16.input_layernorm", | |
| "model.layers.16.mlp.gate", | |
| "model.layers.16.mlp.gate.e_score_correction_bias", | |
| "model.layers.16.post_attention_layernorm", | |
| "model.layers.16.self_attn.forget_gate.A_log", | |
| "model.layers.16.self_attn.b_proj", | |
| "model.layers.16.self_attn.forget_gate.dt_bias", | |
| "model.layers.16.self_attn.f_a_proj", | |
| "model.layers.16.self_attn.f_b_proj", | |
| "model.layers.16.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.16.self_attn.g_a_proj", | |
| "model.layers.16.self_attn.g_b_proj", | |
| "model.layers.16.self_attn.k_conv1d", | |
| "model.layers.16.self_attn.k_proj", | |
| "model.layers.16.self_attn.o_norm", | |
| "model.layers.16.self_attn.o_proj", | |
| "model.layers.16.self_attn.q_conv1d", | |
| "model.layers.16.self_attn.q_proj", | |
| "model.layers.16.self_attn.qkv_proj", | |
| "model.layers.16.self_attn.v_conv1d", | |
| "model.layers.16.self_attn.v_proj", | |
| "model.layers.17.attn_hc.base", | |
| "model.layers.17.attn_hc.fn", | |
| "model.layers.17.attn_hc.scale", | |
| "model.layers.17.ffn_hc.base", | |
| "model.layers.17.ffn_hc.fn", | |
| "model.layers.17.ffn_hc.scale", | |
| "model.layers.17.input_layernorm", | |
| "model.layers.17.mlp.gate", | |
| "model.layers.17.mlp.gate.e_score_correction_bias", | |
| "model.layers.17.post_attention_layernorm", | |
| "model.layers.17.self_attn.forget_gate.A_log", | |
| "model.layers.17.self_attn.b_proj", | |
| "model.layers.17.self_attn.forget_gate.dt_bias", | |
| "model.layers.17.self_attn.f_a_proj", | |
| "model.layers.17.self_attn.f_b_proj", | |
| "model.layers.17.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.17.self_attn.g_a_proj", | |
| "model.layers.17.self_attn.g_b_proj", | |
| "model.layers.17.self_attn.k_conv1d", | |
| "model.layers.17.self_attn.k_proj", | |
| "model.layers.17.self_attn.o_norm", | |
| "model.layers.17.self_attn.o_proj", | |
| "model.layers.17.self_attn.q_conv1d", | |
| "model.layers.17.self_attn.q_proj", | |
| "model.layers.17.self_attn.qkv_proj", | |
| "model.layers.17.self_attn.v_conv1d", | |
| "model.layers.17.self_attn.v_proj", | |
| "model.layers.18.attn_hc.base", | |
| "model.layers.18.attn_hc.fn", | |
| "model.layers.18.attn_hc.scale", | |
| "model.layers.18.ffn_hc.base", | |
| "model.layers.18.ffn_hc.fn", | |
| "model.layers.18.ffn_hc.scale", | |
| "model.layers.18.input_layernorm", | |
| "model.layers.18.mlp.gate", | |
| "model.layers.18.mlp.gate.e_score_correction_bias", | |
| "model.layers.18.post_attention_layernorm", | |
| "model.layers.18.self_attn.forget_gate.A_log", | |
| "model.layers.18.self_attn.b_proj", | |
| "model.layers.18.self_attn.forget_gate.dt_bias", | |
| "model.layers.18.self_attn.f_a_proj", | |
| "model.layers.18.self_attn.f_b_proj", | |
| "model.layers.18.self_attn.fused_qkvbfg_a_proj", | |
| "model.layers.18.self_attn.g_a_proj", | |
| "model.layers.18.self_attn.g_b_proj", | |
| "model.layers.18.self_attn.k_conv1d", | |
| "model.layers.18.self_attn.k_proj", | |
| "model.layers.18.self_attn.o_norm", | |
| "model.layers.18.self_attn.o_proj", | |
| "model.layers.18.self_attn.q_conv1d", | |
| "model.layers.18.self_attn.q_proj", | |
| "model.layers.18.self_attn.qkv_proj", | |
| "model.layers.18.self_attn.v_conv1d", | |
| "model.layers.18.self_attn.v_proj", | |
| "model.layers.19.attn_hc.base", | |
| "model.layers.19.attn_hc.fn", | |
| "model.layers.19.attn_hc.scale", | |
| "model.layers.19.ffn_hc.base", | |
| "model.layers.19.ffn_hc.fn", | |
| "model.layers.19.ffn_hc.scale", | |
| "model.layers.19.input_layernorm", | |
| "model.layers.19.mlp.gate", | |
| "model.layers.19.mlp.gate.e_score_correction_bias", | |
| "model.layers.19.post_attention_layernorm", | |
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| "model.layers.19.self_attn.indexer.index_kpool_compress_gate", | |
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| "model.layers.6.mlp.gate.e_score_correction_bias", | |
| "model.layers.6.post_attention_layernorm", | |
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| "model.layers.7.mlp.gate.e_score_correction_bias", | |
| "model.layers.7.post_attention_layernorm", | |
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| "visual.blocks.7.attn.qkv.bias", | |
| "visual.blocks.7.mlp.down_proj", | |
| "visual.blocks.7.mlp.down_proj.bias", | |
| "visual.blocks.7.mlp.gate_proj", | |
| "visual.blocks.7.mlp.gate_proj.bias", | |
| "visual.blocks.7.mlp.up_proj", | |
| "visual.blocks.7.mlp.up_proj.bias", | |
| "visual.blocks.7.norm1", | |
| "visual.blocks.7.norm2", | |
| "visual.blocks.8.attn.k_norm", | |
| "visual.blocks.8.attn.proj", | |
| "visual.blocks.8.attn.proj.bias", | |
| "visual.blocks.8.attn.q_norm", | |
| "visual.blocks.8.attn.qkv", | |
| "visual.blocks.8.attn.qkv.bias", | |
| "visual.blocks.8.mlp.down_proj", | |
| "visual.blocks.8.mlp.down_proj.bias", | |
| "visual.blocks.8.mlp.gate_proj", | |
| "visual.blocks.8.mlp.gate_proj.bias", | |
| "visual.blocks.8.mlp.up_proj", | |
| "visual.blocks.8.mlp.up_proj.bias", | |
| "visual.blocks.8.norm1", | |
| "visual.blocks.8.norm2", | |
| "visual.blocks.9.attn.k_norm", | |
| "visual.blocks.9.attn.proj", | |
| "visual.blocks.9.attn.proj.bias", | |
| "visual.blocks.9.attn.q_norm", | |
| "visual.blocks.9.attn.qkv", | |
| "visual.blocks.9.attn.qkv.bias", | |
| "visual.blocks.9.mlp.down_proj", | |
| "visual.blocks.9.mlp.down_proj.bias", | |
| "visual.blocks.9.mlp.gate_proj", | |
| "visual.blocks.9.mlp.gate_proj.bias", | |
| "visual.blocks.9.mlp.up_proj", | |
| "visual.blocks.9.mlp.up_proj.bias", | |
| "visual.blocks.9.norm1", | |
| "visual.blocks.9.norm2", | |
| "visual.downsample", | |
| "visual.downsample.bias", | |
| "visual.merger.down_proj", | |
| "visual.merger.gate_proj", | |
| "visual.merger.post_projection_norm", | |
| "visual.merger.post_projection_norm.bias", | |
| "visual.merger.proj", | |
| "visual.merger.up_proj", | |
| "visual.patch_embed.proj", | |
| "visual.patch_embed.proj.bias", | |
| "visual.post_layernorm", | |
| "weights_proj" | |
| ], | |
| "quant_method": "fp8", | |
| "scale_fmt": "float", | |
| "weight_block_size": [ | |
| 32, | |
| 32 | |
| ] | |
| }, | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": null, | |
| "dtype": "bfloat16", | |
| "eos_token_id": [ | |
| 154820, | |
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| ], | |
| "hc_eps": 1e-06, | |
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| "hidden_act": "silu", | |
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| "index_head_dim": 4, | |
| "index_kpool": 4, | |
| "index_kpool_always_select_tail": true, | |
| "index_n_heads": 2, | |
| "index_topk": 8, | |
| "indexer_types": [ | |
| "full", | |
| "full", | |
| "full", | |
| "full" | |
| ], | |
| "initializer_range": 0.02, | |
| "intermediate_size": 32, | |
| "kv_lora_rank": 32, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "deepseek_sparse_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_head_dim": 8, | |
| "linear_lower_bound": -5.0, | |
| "linear_num_heads": 4, | |
| "max_position_embeddings": 1048576, | |
| "mlp_layer_types": [ | |
| "dense", | |
| "dense", | |
| "dense", | |
| "sparse" | |
| ], | |
| "model_type": "glm5_next_text", | |
| "moe_intermediate_size": 64, | |
| "n_group": 1, | |
| "n_routed_experts": 4, | |
| "n_shared_experts": 1, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 4, | |
| "num_experts_per_tok": 2, | |
| "num_hidden_layers": 4, | |
| "num_key_value_heads": 4, | |
| "output_router_logits": false, | |
| "pad_token_id": 154820, | |
| "q_lora_rank": 32, | |
| "qk_head_dim": 8, | |
| "qk_nope_head_dim": 8, | |
| "qk_rope_head_dim": 0, | |
| "rms_norm_eps": 1e-05, | |
| "routed_scaling_factor": 2.5, | |
| "router_aux_loss_coef": 0.001, | |
| "swiglu_limit": 10.0, | |
| "tie_word_embeddings": false, | |
| "topk_group": 1, | |
| "use_cache": true, | |
| "v_head_dim": 8, | |
| "vocab_size": 154880 | |
| }, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.17.0", | |
| "video_end_token_id": 154833, | |
| "video_start_token_id": 154832, | |
| "video_token_id": 154855, | |
| "vision_config": { | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "depth": 2, | |
| "dtype": "bfloat16", | |
| "hidden_act": "silu", | |
| "hidden_size": 16, | |
| "image_size": 448, | |
| "in_channels": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 32, | |
| "model_type": "glm5_next_vision", | |
| "num_heads": 4, | |
| "out_hidden_size": 32, | |
| "patch_size": 14, | |
| "projection_intermediate_size": 32, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "axial" | |
| }, | |
| "spatial_merge_size": 2, | |
| "swiglu_limit": 10.0, | |
| "temporal_patch_size": 2 | |
| } | |
| } | |