Text Generation
Transformers
Safetensors
llama
4bit
bnb
nf4
qlora
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use ping98k/open_llama_3b_v2_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ping98k/open_llama_3b_v2_4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ping98k/open_llama_3b_v2_4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ping98k/open_llama_3b_v2_4bit") model = AutoModelForCausalLM.from_pretrained("ping98k/open_llama_3b_v2_4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ping98k/open_llama_3b_v2_4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ping98k/open_llama_3b_v2_4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ping98k/open_llama_3b_v2_4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ping98k/open_llama_3b_v2_4bit
- SGLang
How to use ping98k/open_llama_3b_v2_4bit 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 "ping98k/open_llama_3b_v2_4bit" \ --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": "ping98k/open_llama_3b_v2_4bit", "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 "ping98k/open_llama_3b_v2_4bit" \ --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": "ping98k/open_llama_3b_v2_4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ping98k/open_llama_3b_v2_4bit with Docker Model Runner:
docker model run hf.co/ping98k/open_llama_3b_v2_4bit
Update tokenizer with Qwen3.5 special tokens + chat template
Browse files- tokenizer.json +99 -0
tokenizer.json
CHANGED
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"rstrip": false,
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"normalized": false,
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"special": true
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}
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],
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"normalizer": null,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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{
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"id": 32000,
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"content": "<|im_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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"id": 32001,
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"content": "<|endoftext|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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{
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"id": 32002,
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"content": "<|im_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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"id": 32003,
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"content": "<think>",
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"lstrip": false,
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"special": true
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"id": 32004,
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"content": "</think>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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{
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"id": 32005,
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"content": "<tool_call>",
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"rstrip": false,
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"id": 32006,
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"content": "</tool_call>",
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"id": 32007,
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"content": "<tool_response>",
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"id": 32008,
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"id": 32009,
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"content": "<tools>",
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"single_word": false,
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"rstrip": false,
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"id": 32010,
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"content": "</tools>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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],
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"normalizer": null,
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