Text Generation
Transformers
Safetensors
PyTorch
llama
facebook
meta
llama-3
text-generation-inference
Instructions to use rubenz-org/llama_3_2_1b_instruct_MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubenz-org/llama_3_2_1b_instruct_MLC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rubenz-org/llama_3_2_1b_instruct_MLC")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rubenz-org/llama_3_2_1b_instruct_MLC") model = AutoModelForCausalLM.from_pretrained("rubenz-org/llama_3_2_1b_instruct_MLC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rubenz-org/llama_3_2_1b_instruct_MLC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rubenz-org/llama_3_2_1b_instruct_MLC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rubenz-org/llama_3_2_1b_instruct_MLC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rubenz-org/llama_3_2_1b_instruct_MLC
- SGLang
How to use rubenz-org/llama_3_2_1b_instruct_MLC 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 "rubenz-org/llama_3_2_1b_instruct_MLC" \ --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": "rubenz-org/llama_3_2_1b_instruct_MLC", "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 "rubenz-org/llama_3_2_1b_instruct_MLC" \ --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": "rubenz-org/llama_3_2_1b_instruct_MLC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rubenz-org/llama_3_2_1b_instruct_MLC with Docker Model Runner:
docker model run hf.co/rubenz-org/llama_3_2_1b_instruct_MLC
File size: 2,347 Bytes
aaa4c1e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | {
"version": "0.1.0",
"model_type": "llama",
"quantization": "q4f16_1",
"model_config": {
"hidden_size": 2048,
"intermediate_size": 8192,
"num_attention_heads": 32,
"num_hidden_layers": 16,
"rms_norm_eps": 1e-05,
"vocab_size": 128256,
"tie_word_embeddings": true,
"position_embedding_base": 500000.0,
"rope_scaling": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"context_window_size": 131072,
"prefill_chunk_size": 8192,
"num_key_value_heads": 8,
"head_dim": 64,
"tensor_parallel_shards": 1,
"pipeline_parallel_stages": 1,
"max_batch_size": 128,
"disaggregation": false
},
"vocab_size": 128256,
"context_window_size": 131072,
"sliding_window_size": -1,
"prefill_chunk_size": 8192,
"attention_sink_size": -1,
"tensor_parallel_shards": 1,
"pipeline_parallel_stages": 1,
"temperature": 0.6,
"presence_penalty": 0.0,
"frequency_penalty": 0.0,
"repetition_penalty": 1.0,
"top_p": 0.9,
"tokenizer_files": [
"tokenizer.json",
"tokenizer_config.json"
],
"tokenizer_info": {
"token_postproc_method": "byte_level",
"prepend_space_in_encode": false,
"strip_space_in_decode": false
},
"conv_template": {
"name": "llama-3",
"system_template": "<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
"system_message": "You are a helpful, respectful and honest assistant.",
"system_prefix_token_ids": [
128000
],
"add_role_after_system_message": true,
"roles": {
"user": "<|start_header_id|>user",
"assistant": "<|start_header_id|>assistant"
},
"role_templates": {
"user": "{user_message}",
"assistant": "{assistant_message}",
"tool": "{tool_message}"
},
"messages": [],
"seps": [
"<|eot_id|>"
],
"role_content_sep": "<|end_header_id|>\n\n",
"role_empty_sep": "<|end_header_id|>\n\n",
"stop_str": [
"<|end_of_text|>",
"<|eot_id|>"
],
"stop_token_ids": [
128001,
128009
],
"function_string": "",
"use_function_calling": false
},
"pad_token_id": 0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
]
} |