Instructions to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Transformers:

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

pipe = pipeline("text-generation", model="OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints

SGLang

How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints 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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \
    --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": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \
        --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": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Docker Model Runner:
```
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
```

OpenNLPLab commited on Jan 10, 2024

Commit

144fe25

1 Parent(s): 127ea79

Add 50B

Browse files

Files changed (1) hide show

README.md +12 -3

README.md CHANGED Viewed

@@ -39,9 +39,18 @@ This official repository unveils the TransNormerLLM3 model along with its open-s
 # Released Weights
-|  param  | token | Hugging Face | Model Scope | Wisemodel |
-| :-----: | :---: | :----------: | :---------: | :-------: |
-| **15B** |  50B  |      🤗       |      🤖      |     🐯     |
 # Benchmark Results
 The evaluations of all models are conducted using the official settings and the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) framework.

 # Released Weights
+|  param  | token |                                                     Hugging Face                                                     | Model Scope | Wisemodel |
+| :-----: | :---: | :------------------------------------------------------------------------------------------------------------------: | :---------: | :-------: |
+| **15B** |  50B  | 🤗[step13000](ttps://huggingface.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints/tree/step13000-50Btokens) |      🤖      |     🐯     |
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", revision='step13000-50Btokens', trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", torch_dtype=torch.bfloat16,revision='step13000-50Btokens', device_map="auto", trust_remote_code=True)
+```
 # Benchmark Results
 The evaluations of all models are conducted using the official settings and the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) framework.