Instructions to use KingWasHereUC/ender-gpt-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingWasHereUC/ender-gpt-weights with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KingWasHereUC/ender-gpt-weights", dtype="auto") - llama-cpp-python
How to use KingWasHereUC/ender-gpt-weights with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KingWasHereUC/ender-gpt-weights", filename="ender_gpt_q4.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use KingWasHereUC/ender-gpt-weights with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KingWasHereUC/ender-gpt-weights # Run inference directly in the terminal: llama-cli -hf KingWasHereUC/ender-gpt-weights
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KingWasHereUC/ender-gpt-weights # Run inference directly in the terminal: llama-cli -hf KingWasHereUC/ender-gpt-weights
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf KingWasHereUC/ender-gpt-weights # Run inference directly in the terminal: ./llama-cli -hf KingWasHereUC/ender-gpt-weights
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf KingWasHereUC/ender-gpt-weights # Run inference directly in the terminal: ./build/bin/llama-cli -hf KingWasHereUC/ender-gpt-weights
Use Docker
docker model run hf.co/KingWasHereUC/ender-gpt-weights
- LM Studio
- Jan
- Ollama
How to use KingWasHereUC/ender-gpt-weights with Ollama:
ollama run hf.co/KingWasHereUC/ender-gpt-weights
- Unsloth Studio
How to use KingWasHereUC/ender-gpt-weights with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KingWasHereUC/ender-gpt-weights to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KingWasHereUC/ender-gpt-weights to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KingWasHereUC/ender-gpt-weights to start chatting
- Docker Model Runner
How to use KingWasHereUC/ender-gpt-weights with Docker Model Runner:
docker model run hf.co/KingWasHereUC/ender-gpt-weights
- Lemonade
How to use KingWasHereUC/ender-gpt-weights with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KingWasHereUC/ender-gpt-weights
Run and chat with the model
lemonade run user.ender-gpt-weights-{{QUANT_TAG}}List all available models
lemonade list
ender-gpt-weights
Browse files- README.md +58 -0
- adapter_config.json +47 -0
- adapter_model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
- training_args.bin +3 -0
README.md
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---
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base_model: microsoft/phi-2
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library_name: transformers
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model_name: ender-gpt-weights
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for ender-gpt-weights
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="KingWasHereUC/ender-gpt-weights", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with SFT.
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### Framework versions
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- TRL: 1.3.0
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- Transformers: 5.8.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.5
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "microsoft/phi-2",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"fc1",
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"dense",
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"q_proj",
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"fc2",
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"v_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d57c34edd71abfbd17cf86678ad036c3974b48382622e1bf5ab8ac7677550cc
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size 94422752
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 2048,
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"pad_token": null,
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"return_token_type_ids": false,
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"tokenizer_class": "CodeGenTokenizer",
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"unk_token": "<|endoftext|>"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:02ed5ee288a333fee4937510f24dbe92fd004d5944376e321e0665968d2fa6a2
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size 5713
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