Instructions to use DiKay/final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DiKay/final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DiKay/final")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DiKay/final") model = AutoModelForCausalLM.from_pretrained("DiKay/final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DiKay/final with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf DiKay/final:F16 # Run inference directly in the terminal: llama cli -hf DiKay/final:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DiKay/final:F16 # Run inference directly in the terminal: llama cli -hf DiKay/final:F16
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 DiKay/final:F16 # Run inference directly in the terminal: ./llama-cli -hf DiKay/final:F16
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 DiKay/final:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf DiKay/final:F16
Use Docker
docker model run hf.co/DiKay/final:F16
- LM Studio
- Jan
- vLLM
How to use DiKay/final with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DiKay/final" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DiKay/final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DiKay/final:F16
- SGLang
How to use DiKay/final 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 "DiKay/final" \ --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": "DiKay/final", "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 "DiKay/final" \ --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": "DiKay/final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use DiKay/final with Ollama:
ollama run hf.co/DiKay/final:F16
- Unsloth Desktop
- Docker Model Runner
How to use DiKay/final with Docker Model Runner:
docker model run hf.co/DiKay/final:F16
- Lemonade
How to use DiKay/final with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DiKay/final:F16
Run and chat with the model
lemonade run user.final-F16
List all available models
lemonade list
- Atomic Chat
Upload 9 files
Browse files- Modelfile +50 -0
- config.json +40 -0
- generation_config.json +7 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +24 -0
- tokenizer_config.json +22 -0
- training_args.bin +3 -0
- vocab.json +0 -0
Modelfile
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#DiKay/myMod
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FROM C:\Users\DetelinkaTrifonova\Desktop\model
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# sets the temperature to 1 [higher is more creative, lower is more coherent]. Default: 0.8, Example: temperature 0.7
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PARAMETER temperature 0.7
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# sets the influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)
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PARAMETER mirostat_eta 0.1
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# sets the controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)
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PARAMETER mirostat_tau 5.0
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# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
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PARAMETER num_ctx 4096
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# sets the maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)
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PARAMETER num_predict 42
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# sets and reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
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PARAMETER top_k 40
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# sets and works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)
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PARAMETER top_p 0.9
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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"""
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# sets a custom system message to specify the behavior of the chat assistant
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SYSTEM You are genius Chat-GPT4o, acting as an assistant. You're as accurate as possible without hallucinations.
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LICENSE DK CORP. LOL
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MESSAGE user
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MESSAGE assistant
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config.json
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{
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"_name_or_path": "DiKay/myMod",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"pad_token_id": 0,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.41.0",
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"use_cache": true,
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"vocab_size": 50257
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"pad_token_id": 0,
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"transformers_version": "4.41.0"
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}
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merges.txt
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a17da846f67e27b68f03c36dfa43fa8b2d5a56d8551961bd8e4992f17c5e9d3
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size 497774208
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|endoftext|>",
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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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"eos_token": {
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"content": "<|endoftext|>",
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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": "<|endoftext|>",
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"unk_token": {
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"content": "<|endoftext|>",
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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_config.json
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{
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"add_bos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"50256": {
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"content": "<|endoftext|>",
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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": "<|endoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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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 sha256:053043db0815aea24292182e77f8d1d3321221520657ff4132ece453e8b72dea
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size 4667
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vocab.json
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