Text Generation
Transformers
Safetensors
llama
facebook
meta
llama-2
conversational
text-generation-inference
aqlm
Instructions to use justheuristic/test-1bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use justheuristic/test-1bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="justheuristic/test-1bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("justheuristic/test-1bit") model = AutoModelForCausalLM.from_pretrained("justheuristic/test-1bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use justheuristic/test-1bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "justheuristic/test-1bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "justheuristic/test-1bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/justheuristic/test-1bit
- SGLang
How to use justheuristic/test-1bit 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 "justheuristic/test-1bit" \ --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": "justheuristic/test-1bit", "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 "justheuristic/test-1bit" \ --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": "justheuristic/test-1bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use justheuristic/test-1bit with Docker Model Runner:
docker model run hf.co/justheuristic/test-1bit
| { | |
| "_name_or_path": "converted_llama2", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pretraining_tp": 1, | |
| "quantization_config": { | |
| "in_group_size": 16, | |
| "linear_weights_not_to_quantize": [ | |
| "model.layers.0.input_layernorm.weight", | |
| "model.layers.0.post_attention_layernorm.weight", | |
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| ], | |
| "nbits_per_codebook": 16, | |
| "num_codebooks": 1, | |
| "out_group_size": 1, | |
| "quant_method": "aqlm" | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.40.1", | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |