Text Generation
Transformers
Safetensors
llama
gsq
gumbel-softmax
quantization
ptq
llama-3.1
vllm
humming
text-generation-inference
Instructions to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ") model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ
- SGLang
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ 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 "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ" \ --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": "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ", "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 "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ" \ --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": "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with Docker Model Runner:
docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ
| { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 128000, | |
| "dtype": "bfloat16", | |
| "eos_token_id": [ | |
| 128001, | |
| 128008, | |
| 128009 | |
| ], | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 8192, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 28672, | |
| "max_position_embeddings": 131072, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 80, | |
| "num_key_value_heads": 8, | |
| "pretraining_tp": 1, | |
| "quantization_config": { | |
| "quant_method": "humming", | |
| "b_dtype": "uint2", | |
| "weight_scale_group_size": 128, | |
| "weight_scale_type": "group", | |
| "has_zero_point": false, | |
| "ignore": [ | |
| "lm_head", | |
| "embed_tokens" | |
| ] | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 8.0, | |
| "high_freq_factor": 4.0, | |
| "low_freq_factor": 1.0, | |
| "original_max_position_embeddings": 8192, | |
| "rope_type": "llama3" | |
| }, | |
| "rope_theta": 500000.0, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.3", | |
| "use_cache": true, | |
| "vocab_size": 128256 | |
| } |