Instructions to use katuni4ka/tiny-random-baichuan2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use katuni4ka/tiny-random-baichuan2 with Transformers:

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

pipe = pipeline("text-generation", model="katuni4ka/tiny-random-baichuan2", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-baichuan2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use katuni4ka/tiny-random-baichuan2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "katuni4ka/tiny-random-baichuan2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-baichuan2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/katuni4ka/tiny-random-baichuan2

SGLang

How to use katuni4ka/tiny-random-baichuan2 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 "katuni4ka/tiny-random-baichuan2" \
    --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": "katuni4ka/tiny-random-baichuan2",
		"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 "katuni4ka/tiny-random-baichuan2" \
        --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": "katuni4ka/tiny-random-baichuan2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use katuni4ka/tiny-random-baichuan2 with Docker Model Runner:
```
docker model run hf.co/katuni4ka/tiny-random-baichuan2
```

katuni4ka commited on Mar 4, 2024

Commit

d799bfc

verified ·

1 Parent(s): e2db34a

Update modeling_baichuan.py

Browse files

Files changed (1) hide show

modeling_baichuan.py +1 -1

modeling_baichuan.py CHANGED Viewed

@@ -59,7 +59,7 @@ def _make_causal_mask(
     Make causal mask used for bi-directional self-attention.
     """
     bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device, dtype=dtype), device=device, dtype=dtype)
     mask_cond = torch.arange(mask.size(-1), device=device)
     mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
     mask = mask.to(dtype)

     Make causal mask used for bi-directional self-attention.
     """
     bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), float("-inf"), device=device)
     mask_cond = torch.arange(mask.size(-1), device=device)
     mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
     mask = mask.to(dtype)