Instructions to use darshanYn/ManualTestCase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darshanYn/ManualTestCase with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("darshanYn/ManualTestCase", dtype="auto") - llama-cpp-python
How to use darshanYn/ManualTestCase with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="darshanYn/ManualTestCase", filename="unsloth.BF16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use darshanYn/ManualTestCase with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf darshanYn/ManualTestCase:Q4_K_M # Run inference directly in the terminal: llama-cli -hf darshanYn/ManualTestCase:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf darshanYn/ManualTestCase:Q4_K_M # Run inference directly in the terminal: llama-cli -hf darshanYn/ManualTestCase:Q4_K_M
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 darshanYn/ManualTestCase:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf darshanYn/ManualTestCase:Q4_K_M
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 darshanYn/ManualTestCase:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf darshanYn/ManualTestCase:Q4_K_M
Use Docker
docker model run hf.co/darshanYn/ManualTestCase:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use darshanYn/ManualTestCase with Ollama:
ollama run hf.co/darshanYn/ManualTestCase:Q4_K_M
- Unsloth Studio new
How to use darshanYn/ManualTestCase 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 darshanYn/ManualTestCase 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 darshanYn/ManualTestCase to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for darshanYn/ManualTestCase to start chatting
- Docker Model Runner
How to use darshanYn/ManualTestCase with Docker Model Runner:
docker model run hf.co/darshanYn/ManualTestCase:Q4_K_M
- Lemonade
How to use darshanYn/ManualTestCase with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull darshanYn/ManualTestCase:Q4_K_M
Run and chat with the model
lemonade run user.ManualTestCase-Q4_K_M
List all available models
lemonade list
(Trained with Unsloth)
Browse files- config.json +36 -2
- model-00001-of-00004.safetensors +2 -2
- model-00002-of-00004.safetensors +2 -2
- model-00003-of-00004.safetensors +2 -2
- model-00004-of-00004.safetensors +1 -1
config.json
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{
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"_name_or_path": "unsloth/meta-llama-3.1-8b-bnb-4bit",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"unsloth_version": "2024.9",
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"use_cache": true,
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"vocab_size": 128256
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}
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model-00001-of-00004.safetensors
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model-00002-of-00004.safetensors
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