Text Generation
Transformers
Safetensors
glm4_moe
agentic-search
retrieval
memory
tool-use
lora
glm
conversational
Instructions to use polygramme/Rolodex-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use polygramme/Rolodex-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="polygramme/Rolodex-12B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("polygramme/Rolodex-12B") model = AutoModelForCausalLM.from_pretrained("polygramme/Rolodex-12B", 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 polygramme/Rolodex-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "polygramme/Rolodex-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "polygramme/Rolodex-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/polygramme/Rolodex-12B
- SGLang
How to use polygramme/Rolodex-12B 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 "polygramme/Rolodex-12B" \ --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": "polygramme/Rolodex-12B", "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 "polygramme/Rolodex-12B" \ --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": "polygramme/Rolodex-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use polygramme/Rolodex-12B with Docker Model Runner:
docker model run hf.co/polygramme/Rolodex-12B
index: map layers.46 MTP tensors to model-mtp.safetensors
Browse files- model.safetensors.index.json +406 -2
model.safetensors.index.json
CHANGED
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"weight_map": {
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"lm_head.weight": "model-00043-of-00043.safetensors",
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"model.norm.weight": "model-00043-of-00043.safetensors"
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}
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}
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{
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"metadata": {
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+
"total_size": 220937661440
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},
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"weight_map": {
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"model.embed_tokens.weight": "model-00001-of-00043.safetensors",
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"model.layers.45.mlp.experts.127.down_proj.weight": "model-00043-of-00043.safetensors",
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"lm_head.weight": "model-00043-of-00043.safetensors",
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"model.norm.weight": "model-00043-of-00043.safetensors",
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"model.layers.46.eh_proj.weight": "model-mtp.safetensors",
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"model.layers.46.embed_tokens.weight": "model-mtp.safetensors",
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"model.layers.46.enorm.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.0.up_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.1.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.1.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.10.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.100.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.100.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.100.up_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.101.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.101.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.102.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.102.up_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.103.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.103.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.108.up_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.109.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.109.gate_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.11.down_proj.weight": "model-mtp.safetensors",
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"model.layers.46.mlp.experts.110.down_proj.weight": "model-mtp.safetensors",
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