Instructions to use RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16") model = AutoModelForCausalLM.from_pretrained("RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16") 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
- vLLM
How to use RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16
- SGLang
How to use RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 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 "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16" \ --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": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16", "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 "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16" \ --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": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 with Docker Model Runner:
docker model run hf.co/RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16
Issue with loading model
Code used:
model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map = 'auto')
Output:
tokenizer_config.json:β100%
β50.9k/50.9kβ[00:00<00:00,β3.50MB/s]
tokenizer.json:β100%
β9.08M/9.08Mβ[00:00<00:00,β29.4MB/s]
special_tokens_map.json:β100%
β296/296β[00:00<00:00,β24.5kB/s]
config.json:β100%
β1.26k/1.26kβ[00:00<00:00,β111kB/s]
model.safetensors:β100%
β5.74G/5.74Gβ[01:53<00:00,β58.5MB/s]
/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:4674: FutureWarning: _is_quantized_training_enabled is going to be deprecated in transformers 4.39.0. Please use model.hf_quantizer.is_trainable instead
warnings.warn(
Some weights of the model checkpoint at neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 were not used when initializing LlamaForCausalLM: ['model.layers.0.mlp.down_proj.bias', 'model.layers.0.mlp.gate_proj.bias', 'model.layers.0.mlp.up_proj.bias', 'model.layers.0.self_attn.k_proj.bias', 'model.layers.0.self_attn.o_proj.bias', 'model.layers.0.self_attn.q_proj.bias', 'model.layers.0.self_attn.v_proj.bias', 'model.layers.1.mlp.down_proj.bias', 'model.layers.1.mlp.gate_proj.bias', 'model.layers.1.mlp.up_proj.bias', 'model.layers.1.self_attn.k_proj.bias', 'model.layers.1.self_attn.o_proj.bias', 'model.layers.1.self_attn.q_proj.bias', 'model.layers.1.self_attn.v_proj.bias', 'model.layers.10.mlp.down_proj.bias', 'model.layers.10.mlp.gate_proj.bias', 'model.layers.10.mlp.up_proj.bias', 'model.layers.10.self_attn.k_proj.bias', 'model.layers.10.self_attn.o_proj.bias', 'model.layers.10.self_attn.q_proj.bias', 'model.layers.10.self_attn.v_proj.bias', 'model.layers.11.mlp.down_proj.bias', 'model.layers.11.mlp.gate_proj.bias', 'model.layers.11.mlp.up_proj.bias', 'model.layers.11.self_attn.k_proj.bias', 'model.layers.11.self_attn.o_proj.bias', 'model.layers.11.self_attn.q_proj.bias', 'model.layers.11.self_attn.v_proj.bias', 'model.layers.12.mlp.down_proj.bias', 'model.layers.12.mlp.gate_proj.bias', 'model.layers.12.mlp.up_proj.bias', 'model.layers.12.self_attn.k_proj.bias', 'model.layers.12.self_attn.o_proj.bias', 'model.layers.12.self_attn.q_proj.bias', 'model.layers.12.self_attn.v_proj.bias', 'model.layers.13.mlp.down_proj.bias', 'model.layers.13.mlp.gate_proj.bias', 'model.layers.13.mlp.up_proj.bias', 'model.layers.13.self_attn.k_proj.bias', 'model.layers.13.self_attn.o_proj.bias', 'model.layers.13.self_attn.q_proj.bias', 'model.layers.13.self_attn.v_proj.bias', 'model.layers.14.mlp.down_proj.bias', 'model.layers.14.mlp.gate_proj.bias', 'model.layers.14.mlp.up_proj.bias', 'model.layers.14.self_attn.k_proj.bias', 'model.layers.14.self_attn.o_proj.bias', 'model.layers.14.self_attn.q_proj.bias', 'model.layers.14.self_attn.v_proj.bias', 'model.layers.15.mlp.down_proj.bias', 'model.layers.15.mlp.gate_proj.bias', 'model.layers.15.mlp.up_proj.bias', 'model.layers.15.self_attn.k_proj.bias', 'model.layers.15.self_attn.o_proj.bias', 'model.layers.15.self_attn.q_proj.bias', 'model.layers.15.self_attn.v_proj.bias', 'model.layers.16.mlp.down_proj.bias', 'model.layers.16.mlp.gate_proj.bias', 'model.layers.16.mlp.up_proj.bias', 'model.layers.16.self_attn.k_proj.bias', 'model.layers.16.self_attn.o_proj.bias', 'model.layers.16.self_attn.q_proj.bias', 'model.layers.16.self_attn.v_proj.bias', 'model.layers.17.mlp.down_proj.bias', 'model.layers.17.mlp.gate_proj.bias', 'model.layers.17.mlp.up_proj.bias', 'model.layers.17.self_attn.k_proj.bias', 'model.layers.17.self_attn.o_proj.bias', 'model.layers.17.self_attn.q_proj.bias', 'model.layers.17.self_attn.v_proj.bias', 'model.layers.18.mlp.down_proj.bias', 'model.layers.18.mlp.gate_proj.bias', 'model.layers.18.mlp.up_proj.bias', 'model.layers.18.self_attn.k_proj.bias', 'model.layers.18.self_attn.o_proj.bias', 'model.layers.18.self_attn.q_proj.bias', 'model.layers.18.self_attn.v_proj.bias', 'model.layers.19.mlp.down_proj.bias', 'model.layers.19.mlp.gate_proj.bias', 'model.layers.19.mlp.up_proj.bias', 'model.layers.19.self_attn.k_proj.bias', 'model.layers.19.self_attn.o_proj.bias', 'model.layers.19.self_attn.q_proj.bias', 'model.layers.19.self_attn.v_proj.bias', 'model.layers.2.mlp.down_proj.bias', 'model.layers.2.mlp.gate_proj.bias', 'model.layers.2.mlp.up_proj.bias', 'model.layers.2.self_attn.k_proj.bias', 'model.layers.2.self_attn.o_proj.bias', 'model.layers.2.self_attn.q_proj.bias', 'model.layers.2.self_attn.v_proj.bias', 'model.layers.20.mlp.down_proj.bias', 'model.layers.20.mlp.gate_proj.bias', 'model.layers.20.mlp.up_proj.bias', 'model.layers.20.self_attn.k_proj.bias', 'model.layers.20.self_attn.o_proj.bias', 'model.layers.20.self_attn.q_proj.bias', 'model.layers.20.self_attn.v_proj.bias', 'model.layers.21.mlp.down_proj.bias', 'model.layers.21.mlp.gate_proj.bias', 'model.layers.21.mlp.up_proj.bias', 'model.layers.21.self_attn.k_proj.bias', 'model.layers.21.self_attn.o_proj.bias', 'model.layers.21.self_attn.q_proj.bias', 'model.layers.21.self_attn.v_proj.bias', 'model.layers.22.mlp.down_proj.bias', 'model.layers.22.mlp.gate_proj.bias', 'model.layers.22.mlp.up_proj.bias', 'model.layers.22.self_attn.k_proj.bias', 'model.layers.22.self_attn.o_proj.bias', 'model.layers.22.self_attn.q_proj.bias', 'model.layers.22.self_attn.v_proj.bias', 'model.layers.23.mlp.down_proj.bias', 'model.layers.23.mlp.gate_proj.bias', 'model.layers.23.mlp.up_proj.bias', 'model.layers.23.self_attn.k_proj.bias', 'model.layers.23.self_attn.o_proj.bias', 'model.layers.23.self_attn.q_proj.bias', 'model.layers.23.self_attn.v_proj.bias', 'model.layers.24.mlp.down_proj.bias', 'model.layers.24.mlp.gate_proj.bias', 'model.layers.24.mlp.up_proj.bias', 'model.layers.24.self_attn.k_proj.bias', 'model.layers.24.self_attn.o_proj.bias', 'model.layers.24.self_attn.q_proj.bias', 'model.layers.24.self_attn.v_proj.bias', 'model.layers.25.mlp.down_proj.bias', 'model.layers.25.mlp.gate_proj.bias', 'model.layers.25.mlp.up_proj.bias', 'model.layers.25.self_attn.k_proj.bias', 'model.layers.25.self_attn.o_proj.bias', 'model.layers.25.self_attn.q_proj.bias', 'model.layers.25.self_attn.v_proj.bias', 'model.layers.26.mlp.down_proj.bias', 'model.layers.26.mlp.gate_proj.bias', 'model.layers.26.mlp.up_proj.bias', 'model.layers.26.self_attn.k_proj.bias', 'model.layers.26.self_attn.o_proj.bias', 'model.layers.26.self_attn.q_proj.bias', 'model.layers.26.self_attn.v_proj.bias', 'model.layers.27.mlp.down_proj.bias', 'model.layers.27.mlp.gate_proj.bias', 'model.layers.27.mlp.up_proj.bias', 'model.layers.27.self_attn.k_proj.bias', 'model.layers.27.self_attn.o_proj.bias', 'model.layers.27.self_attn.q_proj.bias', 'model.layers.27.self_attn.v_proj.bias', 'model.layers.28.mlp.down_proj.bias', 'model.layers.28.mlp.gate_proj.bias', 'model.layers.28.mlp.up_proj.bias', 'model.layers.28.self_attn.k_proj.bias', 'model.layers.28.self_attn.o_proj.bias', 'model.layers.28.self_attn.q_proj.bias', 'model.layers.28.self_attn.v_proj.bias', 'model.layers.29.mlp.down_proj.bias', 'model.layers.29.mlp.gate_proj.bias', 'model.layers.29.mlp.up_proj.bias', 'model.layers.29.self_attn.k_proj.bias', 'model.layers.29.self_attn.o_proj.bias', 'model.layers.29.self_attn.q_proj.bias', 'model.layers.29.self_attn.v_proj.bias', 'model.layers.3.mlp.down_proj.bias', 'model.layers.3.mlp.gate_proj.bias', 'model.layers.3.mlp.up_proj.bias', 'model.layers.3.self_attn.k_proj.bias', 'model.layers.3.self_attn.o_proj.bias', 'model.layers.3.self_attn.q_proj.bias', 'model.layers.3.self_attn.v_proj.bias', 'model.layers.30.mlp.down_proj.bias', 'model.layers.30.mlp.gate_proj.bias', 'model.layers.30.mlp.up_proj.bias', 'model.layers.30.self_attn.k_proj.bias', 'model.layers.30.self_attn.o_proj.bias', 'model.layers.30.self_attn.q_proj.bias', 'model.layers.30.self_attn.v_proj.bias', 'model.layers.31.mlp.down_proj.bias', 'model.layers.31.mlp.gate_proj.bias', 'model.layers.31.mlp.up_proj.bias', 'model.layers.31.self_attn.k_proj.bias', 'model.layers.31.self_attn.o_proj.bias', 'model.layers.31.self_attn.q_proj.bias', 'model.layers.31.self_attn.v_proj.bias', 'model.layers.4.mlp.down_proj.bias', 'model.layers.4.mlp.gate_proj.bias', 'model.layers.4.mlp.up_proj.bias', 'model.layers.4.self_attn.k_proj.bias', 'model.layers.4.self_attn.o_proj.bias', 'model.layers.4.self_attn.q_proj.bias', 'model.layers.4.self_attn.v_proj.bias', 'model.layers.5.mlp.down_proj.bias', 'model.layers.5.mlp.gate_proj.bias', 'model.layers.5.mlp.up_proj.bias', 'model.layers.5.self_attn.k_proj.bias', 'model.layers.5.self_attn.o_proj.bias', 'model.layers.5.self_attn.q_proj.bias', 'model.layers.5.self_attn.v_proj.bias', 'model.layers.6.mlp.down_proj.bias', 'model.layers.6.mlp.gate_proj.bias', 'model.layers.6.mlp.up_proj.bias', 'model.layers.6.self_attn.k_proj.bias', 'model.layers.6.self_attn.o_proj.bias', 'model.layers.6.self_attn.q_proj.bias', 'model.layers.6.self_attn.v_proj.bias', 'model.layers.7.mlp.down_proj.bias', 'model.layers.7.mlp.gate_proj.bias', 'model.layers.7.mlp.up_proj.bias', 'model.layers.7.self_attn.k_proj.bias', 'model.layers.7.self_attn.o_proj.bias', 'model.layers.7.self_attn.q_proj.bias', 'model.layers.7.self_attn.v_proj.bias', 'model.layers.8.mlp.down_proj.bias', 'model.layers.8.mlp.gate_proj.bias', 'model.layers.8.mlp.up_proj.bias', 'model.layers.8.self_attn.k_proj.bias', 'model.layers.8.self_attn.o_proj.bias', 'model.layers.8.self_attn.q_proj.bias', 'model.layers.8.self_attn.v_proj.bias', 'model.layers.9.mlp.down_proj.bias', 'model.layers.9.mlp.gate_proj.bias', 'model.layers.9.mlp.up_proj.bias', 'model.layers.9.self_attn.k_proj.bias', 'model.layers.9.self_attn.o_proj.bias', 'model.layers.9.self_attn.q_proj.bias', 'model.layers.9.self_attn.v_proj.bias']
- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Can anyone help me understand why the weights are not being loaded to these layers? Is it a naming issue?
It takes a long time to complete even if I ignore this warning and use it to generate text.
Any help would be greatly appreciated.
This model is meant to work in vLLM by default, but should work in transformers through the autogptq integration, so I'm not sure what the issue is. Maybe look into issues on the transformers repo with gptq