Text Generation
Transformers
Safetensors
English
llama
Eval Results (legacy)
text-generation-inference
Instructions to use MTSAIR/MultiVerse_70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTSAIR/MultiVerse_70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MTSAIR/MultiVerse_70B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MTSAIR/MultiVerse_70B") model = AutoModelForCausalLM.from_pretrained("MTSAIR/MultiVerse_70B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MTSAIR/MultiVerse_70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MTSAIR/MultiVerse_70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MTSAIR/MultiVerse_70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MTSAIR/MultiVerse_70B
- SGLang
How to use MTSAIR/MultiVerse_70B 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 "MTSAIR/MultiVerse_70B" \ --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": "MTSAIR/MultiVerse_70B", "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 "MTSAIR/MultiVerse_70B" \ --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": "MTSAIR/MultiVerse_70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MTSAIR/MultiVerse_70B with Docker Model Runner:
docker model run hf.co/MTSAIR/MultiVerse_70B
Confirm Qwen, not Llama?
#8
by sealad886 - opened
Hi there, just want to confirm the base model this was trained from? The config.json file has a discrepancy:
{
"_name_or_path": "",
"architectures": [
"LlamaForCausalLM". <--- in Qwen1.5/2 models, this is "Qwen2ForCausalLM"
],
"attention_bias": true,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 8192,
"initializer_range": 0.02,
"intermediate_size": 24576,
"max_position_embeddings": 32768,
"model_type": "llama", <--- in Qwen1.5/2 models, this is "qwen2"
"num_attention_heads": 64,
"num_hidden_layers": 80,
"num_key_value_heads": 64,
"pad_token_id": 151643,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"seq_length": 32768,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.38.2",
"use_cache": true,
"vocab_size": 152064
}
Hi @sealad886 , thanks for your interest !
The initial weights are from Qwen initialized into a Llama class (no much difference in architectures)