Instructions to use budecosystem/code-millenials-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/code-millenials-1b with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/code-millenials-1b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-1b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-1b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use budecosystem/code-millenials-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/code-millenials-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/budecosystem/code-millenials-1b

SGLang

How to use budecosystem/code-millenials-1b 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 "budecosystem/code-millenials-1b" \
    --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": "budecosystem/code-millenials-1b",
		"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 "budecosystem/code-millenials-1b" \
        --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": "budecosystem/code-millenials-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use budecosystem/code-millenials-1b with Docker Model Runner:
```
docker model run hf.co/budecosystem/code-millenials-1b
```

dittops commited on Dec 13, 2023

Commit

473233d

1 Parent(s): 8f39c1d

initial commit

Browse files

Files changed (1) hide show

modeling_phi.py +10 -10

modeling_phi.py CHANGED Viewed

@@ -19,16 +19,16 @@ from transformers.modeling_outputs import CausalLMOutputWithPast
 from .configuration_phi import PhiConfig
-try:
-    from flash_attn.bert_padding import pad_input, unpad_input
-    from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
-    from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
-    from flash_attn.ops.fused_dense import FusedDense
-except:
-    pad_input, unpad_input = None, None
-    FlashRotaryEmbedding = None
-    FlashSelfAttention, FlashCrossAttention = None, None
-    FusedDense = None
 @dataclass

 from .configuration_phi import PhiConfig
+# try:
+#     from flash_attn.bert_padding import pad_input, unpad_input
+#     from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
+#     from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
+#     from flash_attn.ops.fused_dense import FusedDense
+# except:
+pad_input, unpad_input = None, None
+FlashRotaryEmbedding = None
+FlashSelfAttention, FlashCrossAttention = None, None
+FusedDense = None
 @dataclass