Instructions to use trl-internal-testing/tiny-Glm5NextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Upload Glm5NextForConditionalGeneration

by albertvillanova HF Staff - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+26

-1524

Files changed (2) hide show

config.json +24 -1522
model.safetensors +2 -2

config.json CHANGED Viewed

@@ -7,1528 +7,6 @@
   "image_start_token_id": 154830,
   "image_token_id": 154854,
   "model_type": "glm5_next",
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@@ -1578,6 +76,7 @@
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@@ -1586,6 +85,7 @@
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@@ -1595,9 +95,11 @@
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   "model_type": "glm5_next",
   "text_config": {
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     "hc_eps": 1e-06,
     "hc_mult": 4,
     "hc_sinkhorn_iters": 20,
     "index_head_dim": 4,
     "index_kpool": 4,
     "index_kpool_always_select_tail": true,
+    "index_kpool_compress": true,
     "index_n_heads": 2,
+    "index_share_for_mtp_iteration": true,
     "index_topk": 8,
+    "indexer_rope_interleave": true,
     "indexer_types": [
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       "full",
       "linear_attention",
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+      "kda_layers": [
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     "linear_head_dim": 8,
     "linear_lower_bound": -5.0,
     "linear_num_heads": 4,
     "max_position_embeddings": 1048576,
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     "mlp_layer_types": [
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     "model_type": "glm5_next_text",
     "moe_intermediate_size": 64,
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     "n_shared_experts": 1,
     "num_experts_per_tok": 2,
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     "q_lora_rank": 32,
     "rms_norm_eps": 1e-05,
     "routed_scaling_factor": 2.5,
     "router_aux_loss_coef": 0.001,
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     "swiglu_limit": 10.0,
     "tie_word_embeddings": false,
     "topk_group": 1,
+    "topk_method": "noaux_tc",
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     "v_head_dim": 8,
     "vocab_size": 154880

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