Text Generation
Transformers
Safetensors
PyTorch
English
llama
python
java
cpp
sql
function calling
unit tests
causalLM
codeLLAMA modified archi
document
code
code2doc
instruction_tuned
basemodel
docstring
documentation
text-generation-inference
plan
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conversational
Instructions to use PipableAI/pip-code-bandit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PipableAI/pip-code-bandit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PipableAI/pip-code-bandit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-bandit") model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-bandit") 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 PipableAI/pip-code-bandit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PipableAI/pip-code-bandit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PipableAI/pip-code-bandit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PipableAI/pip-code-bandit
- SGLang
How to use PipableAI/pip-code-bandit 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 "PipableAI/pip-code-bandit" \ --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": "PipableAI/pip-code-bandit", "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 "PipableAI/pip-code-bandit" \ --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": "PipableAI/pip-code-bandit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PipableAI/pip-code-bandit with Docker Model Runner:
docker model run hf.co/PipableAI/pip-code-bandit
Upload tokenizer
Browse files- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +193 -0
special_tokens_map.json
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{
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"bos_token": {
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"content": "<|begin▁of▁sentence|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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"eos_token": {
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"content": "<|EOT|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|end▁of▁sentence|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"added_tokens_decoder": {
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"32000": {
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"content": "õ",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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"32001": {
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"content": "÷",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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"32002": {
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"content": "Á",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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"32003": {
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"content": "ý",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32004": {
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"content": "À",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32005": {
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"content": "ÿ",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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"32006": {
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"content": "ø",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32007": {
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"content": "ú",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32008": {
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"content": "þ",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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"32009": {
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"content": "ü",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32010": {
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"content": "ù",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32011": {
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"content": "ö",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32012": {
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"content": "û",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32013": {
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"content": "<|begin▁of▁sentence|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"32014": {
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"content": "<|end▁of▁sentence|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"32015": {
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"content": "<|fim▁hole|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32016": {
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"content": "<|fim▁begin|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32017": {
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"content": "<|fim▁end|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32018": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32019": {
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"content": "<|User|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32020": {
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"content": "<|Assistant|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"32021": {
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"content": "<|EOT|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<|begin▁of▁sentence|>",
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"chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
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| 184 |
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"clean_up_tokenization_spaces": false,
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| 185 |
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"eos_token": "<|EOT|>",
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| 186 |
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"legacy": true,
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| 187 |
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"model_max_length": 16384,
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| 188 |
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"pad_token": "<|end▁of▁sentence|>",
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| 189 |
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"sp_model_kwargs": {},
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| 190 |
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"tokenizer_class": "LlamaTokenizer",
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| 191 |
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"unk_token": null,
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| 192 |
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"use_default_system_prompt": false
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| 193 |
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}
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