Instructions to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT

SGLang

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT 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 "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT" \
    --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": "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT",
		"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 "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT" \
        --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": "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with Docker Model Runner:
```
docker model run hf.co/AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT
```

Pacific-i64 commited on 10 days ago

Commit

cef6641

verified ·

1 Parent(s): e5a599f

Remove forced thinking prefill

Browse files

Files changed (1) hide show

chat_template.jinja +1 -1

chat_template.jinja CHANGED Viewed

@@ -1 +1 @@

- {{- "" -}}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- "System:\n" + (message['content'] | trim) + "\n\n" -}}{%- elif message['role'] == 'user' -%}{{- "User:\n" + (message['content'] | trim) + "\n\n" -}}{%- elif message['role'] == 'assistant' -%}{{- "Assistant:\n" + (message['content'] | trim) + eos_token -}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- "Assistant:\n~~<think>\n~~" -}}{%- endif -%}

+ {{- "" -}}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- "System:\n" + (message['content'] | trim) + "\n\n" -}}{%- elif message['role'] == 'user' -%}{{- "User:\n" + (message['content'] | trim) + "\n\n" -}}{%- elif message['role'] == 'assistant' -%}{{- "Assistant:\n" + (message['content'] | trim) + eos_token -}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- "Assistant:\n" -}}{%- endif -%}