Instructions to use gagan3012/Multirial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gagan3012/Multirial with Transformers:

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

pipe = pipeline("text-generation", model="gagan3012/Multirial")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gagan3012/Multirial")
model = AutoModelForCausalLM.from_pretrained("gagan3012/Multirial", device_map="auto")
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 gagan3012/Multirial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gagan3012/Multirial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/Multirial",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gagan3012/Multirial

SGLang

How to use gagan3012/Multirial 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 "gagan3012/Multirial" \
    --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": "gagan3012/Multirial",
		"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 "gagan3012/Multirial" \
        --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": "gagan3012/Multirial",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gagan3012/Multirial with Docker Model Runner:
```
docker model run hf.co/gagan3012/Multirial
```

gagan3012 commited on Jan 13, 2024

Commit

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@@ -16,7 +16,8 @@ language:
 # Multirial
-This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
 * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
 * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
 * [azale-ai/Starstreak-7b-beta](https://huggingface.co/azale-ai/Starstreak-7b-beta)
@@ -26,59 +27,6 @@ This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/
 * [manishiitg/open-aditi-hi-v1](https://huggingface.co/manishiitg/open-aditi-hi-v1)
 * [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
-## 🧩 Configuration
-```yamlbase_model: gagan3012/Mistral_arabic_dpo
-dtype: bfloat16
-experts:
-- positive_prompts:
-  - chat
-  - assistant
-  - tell me
-  - explain
-  source_model: fblgit/UNA-TheBeagle-7b-v1
-- positive_prompts:
-  - chat
-  - assistant
-  - tell me
-  - explain
-  source_model: openchat/openchat-3.5-0106
-- positive_prompts:
-  - indonesian
-  - indonesia
-  - answer in indonesian
-  source_model: azale-ai/Starstreak-7b-beta
-- positive_prompts:
-  - arabic
-  - arab
-  - arabia
-  - answer in arabic
-  source_model: gagan3012/Mistral_arabic_dpo
-- positive_prompts:
-  - korean
-  - answer in korean
-  - korea
-  source_model: davidkim205/komt-mistral-7b-v1
-- positive_prompts:
-  - chinese
-  - china
-  - answer in chinese
-  source_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
-- positive_prompts:
-  - hindi
-  - india
-  - hindu
-  - answer in hindi
-  source_model: manishiitg/open-aditi-hi-v1
-- positive_prompts:
-  - german
-  - germany
-  - answer in german
-  - deutsch
-  source_model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
-gate_mode: hidden
-```
 ## 💻 Usage
 ```python

 # Multirial
+MultiRial is the first ever multilingual Mixture of experts model.
 * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
 * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
 * [azale-ai/Starstreak-7b-beta](https://huggingface.co/azale-ai/Starstreak-7b-beta)
 * [manishiitg/open-aditi-hi-v1](https://huggingface.co/manishiitg/open-aditi-hi-v1)
 * [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
 ## 💻 Usage
 ```python