Instructions to use DAMO-NLP-MT/polylm-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DAMO-NLP-MT/polylm-13b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DAMO-NLP-MT/polylm-13b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-13b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-13b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DAMO-NLP-MT/polylm-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DAMO-NLP-MT/polylm-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-MT/polylm-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DAMO-NLP-MT/polylm-13b

SGLang

How to use DAMO-NLP-MT/polylm-13b 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 "DAMO-NLP-MT/polylm-13b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-MT/polylm-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DAMO-NLP-MT/polylm-13b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-MT/polylm-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DAMO-NLP-MT/polylm-13b with Docker Model Runner:
```
docker model run hf.co/DAMO-NLP-MT/polylm-13b
```

Model architecture is not same in variant models

by brownyeyez - opened Sep 5, 2023

Discussion

brownyeyez

Sep 5, 2023

In my experiments, i realize that the model architecture of polylm-13b is difference from polylm-multialpaca-13b version, but in model cards, it was said that multialpaca is a SFT version of polylm-13b. This is so confusing that the model type is not matching that in config of base model polylm the LMhead is PolyLMHead, and in SFT version is GPT2LMHead, can you explain clearly about this mismatching?

And if i can using the GPT2LMHead replace for the base model PolyLMHead, and if i can, how can i convert the tensor shape of model to this type of LMHead? (my aims to deploy model with vLLM, supported GPT2LMHead)

This is config of two version, polylm-13b:

{
  "activation_function": "gelu_fast",
  "architectures": [
    "PolyLMHeadModel"
  ],
  "auto_map": {
    "AutoModelForCausalLM": "modeling_polylm.PolyLMHeadModel"
  },
  "attn_pdrop": 0.0,
  "embd_pdrop": 0.0,
  "eos_token_id": 2,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt2",
  "n_ctx": 2048,
  "n_embd": 5120,
  "n_head": 40,
  "n_inner": 20480,
  "n_layer": 40,
  "n_positions": 2048,
  "reorder_and_upcast_attn": false,
  "resid_pdrop": 0.0,
  "scale_attn_by_inverse_layer_idx": false,
  "scale_attn_weights": true,
  "summary_activation": null,
  "summary_first_dropout": 0.0,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "tokenizer_class": "AutoTokenizer",
  "transformers_version": "4.31.0",
  "use_cache": true,
  "vocab_size": 256000
}

and poly-multialpaca-13b:

{
  "activation_function": "gelu_fast",
  "architectures": [
    "GPT2LMHeadModel"
  ],
  "attn_pdrop": 0.0,
  "bos_token_id": 255999,
  "embd_pdrop": 0.0,
  "eos_token_id": 255999,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt2",
  "n_embd": 5120,
  "n_head": 40,
  "n_inner": 20480,
  "n_layer": 40,
  "n_positions": 2048,
  "reorder_and_upcast_attn": false,
  "resid_pdrop": 0.0,
  "scale_attn_by_inverse_layer_idx": false,
  "scale_attn_weights": true,
  "summary_activation": null,
  "summary_first_dropout": 0.0,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "tokenizer_class": "AutoTokenizer",
  "transformers_version": "4.29.2",
  "use_cache": true,
  "vocab_size": 256000
}

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