Instructions to use Monike123/LLaMAbyte-DS_v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Monike123/LLaMAbyte-DS_v8 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 Monike123/LLaMAbyte-DS_v8:Q4_K_M # Run inference directly in the terminal: llama cli -hf Monike123/LLaMAbyte-DS_v8:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Monike123/LLaMAbyte-DS_v8:Q4_K_M # Run inference directly in the terminal: llama cli -hf Monike123/LLaMAbyte-DS_v8: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 Monike123/LLaMAbyte-DS_v8:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Monike123/LLaMAbyte-DS_v8: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 Monike123/LLaMAbyte-DS_v8:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Monike123/LLaMAbyte-DS_v8:Q4_K_M
Use Docker
docker model run hf.co/Monike123/LLaMAbyte-DS_v8:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Monike123/LLaMAbyte-DS_v8 with Ollama:
ollama run hf.co/Monike123/LLaMAbyte-DS_v8:Q4_K_M
- Unsloth Studio
How to use Monike123/LLaMAbyte-DS_v8 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 Monike123/LLaMAbyte-DS_v8 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 Monike123/LLaMAbyte-DS_v8 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Monike123/LLaMAbyte-DS_v8 to start chatting
- Docker Model Runner
How to use Monike123/LLaMAbyte-DS_v8 with Docker Model Runner:
docker model run hf.co/Monike123/LLaMAbyte-DS_v8:Q4_K_M
- Lemonade
How to use Monike123/LLaMAbyte-DS_v8 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Monike123/LLaMAbyte-DS_v8:Q4_K_M
Run and chat with the model
lemonade run user.LLaMAbyte-DS_v8-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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| "format_accuracy": 0.0, | |
| "precision": 1.0, | |
| "recall": 0.7647058823529411, | |
| "f1": 0.8666666666666667, | |
| "avg_rouge1": 0.03211764705882353, | |
| "avg_rouge2": 0.006058823529411765, | |
| "avg_rougeL": 0.030941176470588236, | |
| "avg_response_time": 16.512235294117644 | |
| }, | |
| "per_category": { | |
| "DS Logic": { | |
| "accuracy": 1.0, | |
| "format": 0.0, | |
| "rougeL": 0.022, | |
| "avg_time": 15.978, | |
| "n": 2 | |
| }, | |
| "LangChain": { | |
| "accuracy": 0.25, | |
| "format": 0.0, | |
| "rougeL": 0.022, | |
| "avg_time": 17.225, | |
| "n": 4 | |
| }, | |
| "ML": { | |
| "accuracy": 1.0, | |
| "format": 0.0, | |
| "rougeL": 0.031, | |
| "avg_time": 22.483, | |
| "n": 3 | |
| }, | |
| "Pandas": { | |
| "accuracy": 1.0, | |
| "format": 0.0, | |
| "rougeL": 0.031, | |
| "avg_time": 17.45, | |
| "n": 4 | |
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| "R": { | |
| "accuracy": 0.5, | |
| "format": 0.0, | |
| "rougeL": 0.039, | |
| "avg_time": 12.786, | |
| "n": 2 | |
| }, | |
| "Viz": { | |
| "accuracy": 1.0, | |
| "format": 0.0, | |
| "rougeL": 0.05, | |
| "avg_time": 8.516, | |
| "n": 2 | |
| } | |
| }, | |
| "per_sample": [ | |
| { | |
| "category": "LangChain", | |
| "prompt": "What function limits the number of messages passed to a model by counting tokens in Langchain? in Python ?", | |
| "keyword": "trim_messages", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.0, | |
| "rouge2": 0.0, | |
| "rougeL": 0.0, | |
| "keyword_match": 0, | |
| "response_time": 20.159, | |
| "output_tokens": 190, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "LangChain", | |
| "prompt": "What argument enables jq schema parsing for content_key in JSONLoader in Langchain? in Python ?", | |
| "keyword": "is_content_key_jq_parsable", | |
| "library": "", | |
| "code": "", | |
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| "rouge1": 0.081, | |
| "rouge2": 0.021, | |
| "rougeL": 0.061, | |
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| "response_time": 17.154, | |
| "output_tokens": 137, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "LangChain", | |
| "prompt": "What callback handler tracks token usage in Langchain? in Python ?", | |
| "keyword": "UsageMetadataCallbackHandler", | |
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| "code": "", | |
| "math": "", | |
| "rouge1": 0.0, | |
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| "output_tokens": 183, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "LangChain", | |
| "prompt": "What taxonomy is used on GitHub Issues for sorting and discovery in Langchain? in Python ?", | |
| "keyword": "Labels", | |
| "library": "", | |
| "code": "", | |
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| "rouge1": 0.028, | |
| "rouge2": 0.0, | |
| "rougeL": 0.028, | |
| "keyword_match": 1, | |
| "response_time": 12.577, | |
| "output_tokens": 123, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Pandas", | |
| "prompt": "How to drop missing values in a pandas DataFrame? in Python ?", | |
| "keyword": "dropna", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.021, | |
| "rouge2": 0.0, | |
| "rougeL": 0.021, | |
| "keyword_match": 1, | |
| "response_time": 20.368, | |
| "output_tokens": 200, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Pandas", | |
| "prompt": "How to read a CSV file using pandas? in Python ?", | |
| "keyword": "read_csv", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.061, | |
| "rouge2": 0.031, | |
| "rougeL": 0.061, | |
| "keyword_match": 1, | |
| "response_time": 10.138, | |
| "output_tokens": 98, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Pandas", | |
| "prompt": "How to rename a column in pandas? in Python ?", | |
| "keyword": "rename", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.023, | |
| "rouge2": 0.0, | |
| "rougeL": 0.023, | |
| "keyword_match": 1, | |
| "response_time": 19.195, | |
| "output_tokens": 182, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Pandas", | |
| "prompt": "How to group rows and compute aggregate in pandas? in Python ?", | |
| "keyword": "groupby", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.019, | |
| "rouge2": 0.0, | |
| "rougeL": 0.019, | |
| "keyword_match": 1, | |
| "response_time": 20.097, | |
| "output_tokens": 200, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "ML", | |
| "prompt": "How to train a decision tree classifier in sklearn? in Python ?", | |
| "keyword": "DecisionTreeClassifier", | |
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| "code": "", | |
| "math": "", | |
| "rouge1": 0.019, | |
| "rouge2": 0.0, | |
| "rougeL": 0.019, | |
| "keyword_match": 1, | |
| "response_time": 20.672, | |
| "output_tokens": 200, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "ML", | |
| "prompt": "How to split dataset into train and test in sklearn? in Python ?", | |
| "keyword": "train_test_split", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.048, | |
| "rouge2": 0.033, | |
| "rougeL": 0.048, | |
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| "format_correct": 0 | |
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| { | |
| "category": "ML", | |
| "prompt": "How to normalize features using StandardScaler in sklearn? in Python ?", | |
| "keyword": "StandardScaler", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
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| "rouge2": 0.0, | |
| "rougeL": 0.025, | |
| "keyword_match": 1, | |
| "response_time": 25.984, | |
| "output_tokens": 136, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Viz", | |
| "prompt": "How to plot a heatmap using seaborn? in Python ?", | |
| "keyword": "heatmap", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.043, | |
| "rouge2": 0.0, | |
| "rougeL": 0.043, | |
| "keyword_match": 1, | |
| "response_time": 9.108, | |
| "output_tokens": 87, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "Viz", | |
| "prompt": "How to create a histogram using matplotlib? in Python ?", | |
| "keyword": "hist", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.057, | |
| "rouge2": 0.0, | |
| "rougeL": 0.057, | |
| "keyword_match": 1, | |
| "response_time": 7.924, | |
| "output_tokens": 77, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "R", | |
| "prompt": "How to get the maximum value of a vector in R ?", | |
| "keyword": "max(", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.043, | |
| "rouge2": 0.0, | |
| "rougeL": 0.043, | |
| "keyword_match": 1, | |
| "response_time": 8.151, | |
| "output_tokens": 75, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "R", | |
| "prompt": "How to read a CSV file in R ?", | |
| "keyword": "read.csv", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.035, | |
| "rouge2": 0.018, | |
| "rougeL": 0.035, | |
| "keyword_match": 0, | |
| "response_time": 17.421, | |
| "output_tokens": 172, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "DS Logic", | |
| "prompt": "What does overfitting mean in machine learning? in Python ?", | |
| "keyword": "overfit", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.026, | |
| "rouge2": 0.0, | |
| "rougeL": 0.026, | |
| "keyword_match": 1, | |
| "response_time": 11.186, | |
| "output_tokens": 106, | |
| "format_correct": 0 | |
| }, | |
| { | |
| "category": "DS Logic", | |
| "prompt": "What is gradient descent in machine learning? in Python ?", | |
| "keyword": "gradient", | |
| "library": "", | |
| "code": "", | |
| "math": "", | |
| "rouge1": 0.017, | |
| "rouge2": 0.0, | |
| "rougeL": 0.017, | |
| "keyword_match": 1, | |
| "response_time": 20.77, | |
| "output_tokens": 196, | |
| "format_correct": 0 | |
| } | |
| ] | |
| } |