Instructions to use jetmoe/jetmoe-8b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jetmoe/jetmoe-8b-chat with Transformers:

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

pipe = pipeline("text-generation", model="jetmoe/jetmoe-8b-chat", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jetmoe/jetmoe-8b-chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jetmoe/jetmoe-8b-chat", trust_remote_code=True)
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 jetmoe/jetmoe-8b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jetmoe/jetmoe-8b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jetmoe/jetmoe-8b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jetmoe/jetmoe-8b-chat

SGLang

How to use jetmoe/jetmoe-8b-chat 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 "jetmoe/jetmoe-8b-chat" \
    --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": "jetmoe/jetmoe-8b-chat",
		"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 "jetmoe/jetmoe-8b-chat" \
        --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": "jetmoe/jetmoe-8b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jetmoe/jetmoe-8b-chat with Docker Model Runner:
```
docker model run hf.co/jetmoe/jetmoe-8b-chat
```

Guo commited on Apr 2, 2024

Commit

fd09ce7

1 Parent(s): b5a6954

walk around for import check

Browse files

Files changed (1) hide show

modeling_jetmoe.py +8 -4

modeling_jetmoe.py CHANGED Viewed

@@ -9,7 +9,6 @@ from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
 from torch.nn import functional as F
-#import megablocks
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
@@ -30,9 +29,14 @@ from transformers.cache_utils import Cache, DynamicCache
 from .configuration_jetmoe import JetMoEConfig
 from . import moe
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
 logger = logging.get_logger(__name__)

 from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
 from torch.nn import functional as F
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
 from .configuration_jetmoe import JetMoEConfig
 from . import moe
+try:
+    if is_flash_attn_2_available():
+        from flash_attn import flash_attn_func, flash_attn_varlen_func
+        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+except ImportError:
+    # Workaround for https://github.com/huggingface/transformers/issues/28459,
+    # don't move to contextlib.suppress(ImportError)
+    pass
 logger = logging.get_logger(__name__)