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
Add v8 LoRA adapter + evaluation plots
Browse files- .gitattributes +5 -0
- adapter/README.md +202 -0
- adapter/adapter_config.json +29 -0
- adapter/adapter_model.safetensors +3 -0
- adapter/eval_plots/eval_log.json +50 -0
- adapter/eval_plots/eval_results.json +314 -0
- adapter/eval_plots/loss_log.json +482 -0
- adapter/eval_plots/plot_01_train_val_loss.png +0 -0
- adapter/eval_plots/plot_02_loss_smoothed.png +0 -0
- adapter/eval_plots/plot_03_overall_metrics.png +0 -0
- adapter/eval_plots/plot_04_per_category.png +0 -0
- adapter/eval_plots/plot_05_category_response_time.png +0 -0
- adapter/eval_plots/plot_06_rouge_heatmap.png +3 -0
- adapter/eval_plots/plot_07_keyword_format_match.png +3 -0
- adapter/eval_plots/plot_08_response_times.png +3 -0
- adapter/eval_plots/plot_09_token_distribution.png +0 -0
- adapter/eval_plots/plot_10_confusion_matrix.png +0 -0
- adapter/eval_plots/plot_11_radar_chart.png +3 -0
- adapter/eval_plots/plot_12_dashboard.png +3 -0
- adapter/special_tokens_map.json +30 -0
- adapter/tokenizer.json +0 -0
- adapter/tokenizer.model +3 -0
- adapter/tokenizer_config.json +84 -0
.gitattributes
CHANGED
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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adapter/eval_plots/plot_06_rouge_heatmap.png filter=lfs diff=lfs merge=lfs -text
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adapter/eval_plots/plot_07_keyword_format_match.png filter=lfs diff=lfs merge=lfs -text
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adapter/eval_plots/plot_08_response_times.png filter=lfs diff=lfs merge=lfs -text
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adapter/eval_plots/plot_11_radar_chart.png filter=lfs diff=lfs merge=lfs -text
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adapter/eval_plots/plot_12_dashboard.png filter=lfs diff=lfs merge=lfs -text
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adapter/README.md
ADDED
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| 1 |
+
---
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| 2 |
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base_model: codellama/CodeLlama-7b-Instruct-hf
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| 3 |
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library_name: peft
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| 4 |
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---
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| 5 |
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| 6 |
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# Model Card for Model ID
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| 7 |
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| 8 |
+
<!-- Provide a quick summary of what the model is/does. -->
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| 9 |
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| 10 |
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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- **Developed by:** [More Information Needed]
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| 21 |
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- **Funded by [optional]:** [More Information Needed]
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| 22 |
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- **Shared by [optional]:** [More Information Needed]
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| 23 |
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- **Model type:** [More Information Needed]
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| 24 |
+
- **Language(s) (NLP):** [More Information Needed]
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| 25 |
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- **License:** [More Information Needed]
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| 26 |
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- **Finetuned from model [optional]:** [More Information Needed]
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| 27 |
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| 28 |
+
### Model Sources [optional]
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| 29 |
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| 30 |
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<!-- Provide the basic links for the model. -->
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| 31 |
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| 32 |
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- **Repository:** [More Information Needed]
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| 33 |
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- **Paper [optional]:** [More Information Needed]
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| 34 |
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- **Demo [optional]:** [More Information Needed]
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| 35 |
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| 36 |
+
## Uses
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| 37 |
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| 38 |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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| 39 |
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| 40 |
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### Direct Use
|
| 41 |
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| 42 |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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| 43 |
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| 44 |
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[More Information Needed]
|
| 45 |
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| 46 |
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### Downstream Use [optional]
|
| 47 |
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|
| 48 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 49 |
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| 50 |
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[More Information Needed]
|
| 51 |
+
|
| 52 |
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### Out-of-Scope Use
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| 53 |
+
|
| 54 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 55 |
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|
| 56 |
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[More Information Needed]
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| 57 |
+
|
| 58 |
+
## Bias, Risks, and Limitations
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| 59 |
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| 60 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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| 61 |
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| 62 |
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[More Information Needed]
|
| 63 |
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| 64 |
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### Recommendations
|
| 65 |
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| 66 |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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| 67 |
+
|
| 68 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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| 69 |
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| 70 |
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## How to Get Started with the Model
|
| 71 |
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| 72 |
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Use the code below to get started with the model.
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| 73 |
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| 74 |
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[More Information Needed]
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| 76 |
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## Training Details
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| 77 |
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| 78 |
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### Training Data
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| 79 |
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| 80 |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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| 94 |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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| 96 |
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| 97 |
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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| 100 |
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[More Information Needed]
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| 102 |
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| 103 |
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## Evaluation
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| 104 |
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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| 108 |
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#### Testing Data
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| 110 |
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| 111 |
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<!-- This should link to a Dataset Card if possible. -->
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| 112 |
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| 113 |
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[More Information Needed]
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| 114 |
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| 115 |
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#### Factors
|
| 116 |
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| 117 |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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| 118 |
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[More Information Needed]
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| 120 |
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#### Metrics
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| 122 |
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| 123 |
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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| 126 |
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### Results
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| 128 |
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[More Information Needed]
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| 130 |
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#### Summary
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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## Model Examination [optional]
|
| 136 |
+
|
| 137 |
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<!-- Relevant interpretability work for the model goes here -->
|
| 138 |
+
|
| 139 |
+
[More Information Needed]
|
| 140 |
+
|
| 141 |
+
## Environmental Impact
|
| 142 |
+
|
| 143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 144 |
+
|
| 145 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
| 146 |
+
|
| 147 |
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- **Hardware Type:** [More Information Needed]
|
| 148 |
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- **Hours used:** [More Information Needed]
|
| 149 |
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- **Cloud Provider:** [More Information Needed]
|
| 150 |
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- **Compute Region:** [More Information Needed]
|
| 151 |
+
- **Carbon Emitted:** [More Information Needed]
|
| 152 |
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|
| 153 |
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## Technical Specifications [optional]
|
| 154 |
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|
| 155 |
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### Model Architecture and Objective
|
| 156 |
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|
| 157 |
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[More Information Needed]
|
| 158 |
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|
| 159 |
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### Compute Infrastructure
|
| 160 |
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| 161 |
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[More Information Needed]
|
| 162 |
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|
| 163 |
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#### Hardware
|
| 164 |
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|
| 165 |
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[More Information Needed]
|
| 166 |
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| 167 |
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#### Software
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| 168 |
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| 169 |
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[More Information Needed]
|
| 170 |
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|
| 171 |
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## Citation [optional]
|
| 172 |
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|
| 173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 174 |
+
|
| 175 |
+
**BibTeX:**
|
| 176 |
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|
| 177 |
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[More Information Needed]
|
| 178 |
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|
| 179 |
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**APA:**
|
| 180 |
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|
| 181 |
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[More Information Needed]
|
| 182 |
+
|
| 183 |
+
## Glossary [optional]
|
| 184 |
+
|
| 185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 186 |
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|
| 187 |
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[More Information Needed]
|
| 188 |
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|
| 189 |
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## More Information [optional]
|
| 190 |
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|
| 191 |
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[More Information Needed]
|
| 192 |
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|
| 193 |
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## Model Card Authors [optional]
|
| 194 |
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|
| 195 |
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[More Information Needed]
|
| 196 |
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|
| 197 |
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## Model Card Contact
|
| 198 |
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|
| 199 |
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[More Information Needed]
|
| 200 |
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### Framework versions
|
| 201 |
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|
| 202 |
+
- PEFT 0.13.2
|
adapter/adapter_config.json
ADDED
|
@@ -0,0 +1,29 @@
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| 1 |
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{
|
| 2 |
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"alpha_pattern": {},
|
| 3 |
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"auto_mapping": null,
|
| 4 |
+
"base_model_name_or_path": "codellama/CodeLlama-7b-Instruct-hf",
|
| 5 |
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"bias": "none",
|
| 6 |
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"fan_in_fan_out": false,
|
| 7 |
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"inference_mode": true,
|
| 8 |
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"init_lora_weights": true,
|
| 9 |
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"layer_replication": null,
|
| 10 |
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"layers_pattern": null,
|
| 11 |
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"layers_to_transform": null,
|
| 12 |
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"loftq_config": {},
|
| 13 |
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"lora_alpha": 16,
|
| 14 |
+
"lora_dropout": 0.05,
|
| 15 |
+
"megatron_config": null,
|
| 16 |
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"megatron_core": "megatron.core",
|
| 17 |
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"modules_to_save": null,
|
| 18 |
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"peft_type": "LORA",
|
| 19 |
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"r": 8,
|
| 20 |
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"rank_pattern": {},
|
| 21 |
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"revision": null,
|
| 22 |
+
"target_modules": [
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
+
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|
| 28 |
+
"use_rslora": false
|
| 29 |
+
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|
adapter/adapter_model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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adapter/eval_plots/eval_log.json
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| 1 |
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[
|
| 2 |
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|
| 4 |
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|
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|
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|
| 16 |
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| 17 |
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| 19 |
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|
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| 30 |
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| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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"step": 1000,
|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 44 |
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| 45 |
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adapter/eval_plots/eval_results.json
ADDED
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|
| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 6 |
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| 14 |
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| 15 |
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| 18 |
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|
| 19 |
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|
| 20 |
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| 22 |
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| 23 |
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|
| 27 |
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| 28 |
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| 29 |
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|
| 30 |
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| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
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|
| 39 |
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|
| 40 |
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"n": 4
|
| 41 |
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|
| 42 |
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| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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},
|
| 57 |
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|
| 58 |
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{
|
| 59 |
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"category": "LangChain",
|
| 60 |
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"prompt": "What function limits the number of messages passed to a model by counting tokens in Langchain? in Python ?",
|
| 61 |
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"keyword": "trim_messages",
|
| 62 |
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"library": "import nltk",
|
| 63 |
+
"code": "nltk.LangChain(5) \n.add_rule('[A-Za-z]+', '[A-Za-z]+') \n.add_rule('[0-9]+', '[0-9]+') \n.add_rule('[A-Za-z0-9]+', '[A-Za-z0-9]+') \n.add_rule('[A-Za-z0-9\\.]+', '[A-Za-z0-9\\.]+') \n.add_rule('[A-Za-z",
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| 64 |
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"math": "",
|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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"format_correct": 0
|
| 72 |
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},
|
| 73 |
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{
|
| 74 |
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"category": "LangChain",
|
| 75 |
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"prompt": "What argument enables jq schema parsing for content_key in JSONLoader in Langchain? in Python ?",
|
| 76 |
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"keyword": "is_content_key_jq_parsable",
|
| 77 |
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"library": "import pandas as pd",
|
| 78 |
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"code": "df = pd.read_json('file.json', orient='records')",
|
| 79 |
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"math": "",
|
| 80 |
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"rouge1": 0.0,
|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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},
|
| 88 |
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{
|
| 89 |
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"category": "LangChain",
|
| 90 |
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"prompt": "What callback handler tracks token usage in Langchain? in Python ?",
|
| 91 |
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"keyword": "UsageMetadataCallbackHandler",
|
| 92 |
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"library": "TensorFlow",
|
| 93 |
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"code": "tf.keras.callbacks.Callback()",
|
| 94 |
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"math": "",
|
| 95 |
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|
| 96 |
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| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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},
|
| 103 |
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{
|
| 104 |
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"category": "LangChain",
|
| 105 |
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"prompt": "What taxonomy is used on GitHub Issues for sorting and discovery in Langchain? in Python ?",
|
| 106 |
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"keyword": "Labels",
|
| 107 |
+
"library": "import pandas as pd",
|
| 108 |
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"code": "df.sort_values('col', ascending=True).head()",
|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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{
|
| 119 |
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|
| 120 |
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"prompt": "How to drop missing values in a pandas DataFrame? in Python ?",
|
| 121 |
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"keyword": "dropna",
|
| 122 |
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"library": "pd",
|
| 123 |
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"code": "df.dropna(inplace=True) \n.",
|
| 124 |
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|
| 125 |
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|
| 126 |
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|
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|
| 129 |
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|
| 130 |
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|
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|
| 132 |
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|
| 133 |
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{
|
| 134 |
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"category": "Pandas",
|
| 135 |
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"prompt": "How to read a CSV file using pandas? in Python ?",
|
| 136 |
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"keyword": "read_csv",
|
| 137 |
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"library": "import pandas as pd",
|
| 138 |
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"code": "df = pd.read_csv('file.csv') \n.",
|
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|
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|
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|
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|
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|
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|
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|
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|
| 148 |
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{
|
| 149 |
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"category": "Pandas",
|
| 150 |
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"prompt": "How to rename a column in pandas? in Python ?",
|
| 151 |
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"keyword": "rename",
|
| 152 |
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"library": "import pandas as pd",
|
| 153 |
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"code": "df.rename(columns={'old_col': 'new_col'}, inplace=True) \n.",
|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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{
|
| 164 |
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"category": "Pandas",
|
| 165 |
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"prompt": "How to group rows and compute aggregate in pandas? in Python ?",
|
| 166 |
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"keyword": "groupby",
|
| 167 |
+
"library": "import pandas as pd",
|
| 168 |
+
"code": "df.groupby('category').agg({'value': 'sum'}) \n.",
|
| 169 |
+
"math": "",
|
| 170 |
+
"rouge1": 0.286,
|
| 171 |
+
"rouge2": 0.0,
|
| 172 |
+
"rougeL": 0.286,
|
| 173 |
+
"keyword_match": 1,
|
| 174 |
+
"response_time": 16.524,
|
| 175 |
+
"output_tokens": 200,
|
| 176 |
+
"format_correct": 0
|
| 177 |
+
},
|
| 178 |
+
{
|
| 179 |
+
"category": "ML",
|
| 180 |
+
"prompt": "How to train a decision tree classifier in sklearn? in Python ?",
|
| 181 |
+
"keyword": "DecisionTreeClassifier",
|
| 182 |
+
"library": "from sklearn.tree import DecisionTreeClassifier",
|
| 183 |
+
"code": "clf = DecisionTreeClassifier()",
|
| 184 |
+
"math": "",
|
| 185 |
+
"rouge1": 0.667,
|
| 186 |
+
"rouge2": 0.0,
|
| 187 |
+
"rougeL": 0.667,
|
| 188 |
+
"keyword_match": 1,
|
| 189 |
+
"response_time": 16.391,
|
| 190 |
+
"output_tokens": 200,
|
| 191 |
+
"format_correct": 0
|
| 192 |
+
},
|
| 193 |
+
{
|
| 194 |
+
"category": "ML",
|
| 195 |
+
"prompt": "How to split dataset into train and test in sklearn? in Python ?",
|
| 196 |
+
"keyword": "train_test_split",
|
| 197 |
+
"library": "from sklearn.model_selection import train_test_split",
|
| 198 |
+
"code": "X_train, X_test = train_test_split(df, test_size=0.2)",
|
| 199 |
+
"math": "",
|
| 200 |
+
"rouge1": 0.4,
|
| 201 |
+
"rouge2": 0.308,
|
| 202 |
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"rougeL": 0.4,
|
| 203 |
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"keyword_match": 1,
|
| 204 |
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"response_time": 17.126,
|
| 205 |
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"output_tokens": 200,
|
| 206 |
+
"format_correct": 0
|
| 207 |
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},
|
| 208 |
+
{
|
| 209 |
+
"category": "ML",
|
| 210 |
+
"prompt": "How to normalize features using StandardScaler in sklearn? in Python ?",
|
| 211 |
+
"keyword": "StandardScaler",
|
| 212 |
+
"library": "from sklearn.preprocessing import StandardScaler",
|
| 213 |
+
"code": "scaler = StandardScaler(); X_scaled = scaler.fit_transform(X)",
|
| 214 |
+
"math": "",
|
| 215 |
+
"rouge1": 0.222,
|
| 216 |
+
"rouge2": 0.0,
|
| 217 |
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"rougeL": 0.222,
|
| 218 |
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"keyword_match": 1,
|
| 219 |
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"response_time": 16.996,
|
| 220 |
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"output_tokens": 200,
|
| 221 |
+
"format_correct": 0
|
| 222 |
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},
|
| 223 |
+
{
|
| 224 |
+
"category": "Viz",
|
| 225 |
+
"prompt": "How to plot a heatmap using seaborn? in Python ?",
|
| 226 |
+
"keyword": "heatmap",
|
| 227 |
+
"library": "import seaborn as sns",
|
| 228 |
+
"code": "sns.heatmap(df, annot=True)",
|
| 229 |
+
"math": "",
|
| 230 |
+
"rouge1": 0.333,
|
| 231 |
+
"rouge2": 0.0,
|
| 232 |
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"rougeL": 0.333,
|
| 233 |
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"keyword_match": 1,
|
| 234 |
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"response_time": 16.279,
|
| 235 |
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"output_tokens": 200,
|
| 236 |
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"format_correct": 0
|
| 237 |
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},
|
| 238 |
+
{
|
| 239 |
+
"category": "Viz",
|
| 240 |
+
"prompt": "How to create a histogram using matplotlib? in Python ?",
|
| 241 |
+
"keyword": "hist",
|
| 242 |
+
"library": "import matplotlib.pyplot as plt",
|
| 243 |
+
"code": "plt.hist(data, bins=5)",
|
| 244 |
+
"math": "",
|
| 245 |
+
"rouge1": 0.333,
|
| 246 |
+
"rouge2": 0.0,
|
| 247 |
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"rougeL": 0.333,
|
| 248 |
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"keyword_match": 1,
|
| 249 |
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"response_time": 16.764,
|
| 250 |
+
"output_tokens": 200,
|
| 251 |
+
"format_correct": 0
|
| 252 |
+
},
|
| 253 |
+
{
|
| 254 |
+
"category": "R",
|
| 255 |
+
"prompt": "How to get the maximum value of a vector in R ?",
|
| 256 |
+
"keyword": "max(",
|
| 257 |
+
"library": "",
|
| 258 |
+
"code": "max_value <- max(vector)",
|
| 259 |
+
"math": "",
|
| 260 |
+
"rouge1": 0.4,
|
| 261 |
+
"rouge2": 0.0,
|
| 262 |
+
"rougeL": 0.4,
|
| 263 |
+
"keyword_match": 1,
|
| 264 |
+
"response_time": 15.953,
|
| 265 |
+
"output_tokens": 200,
|
| 266 |
+
"format_correct": 0
|
| 267 |
+
},
|
| 268 |
+
{
|
| 269 |
+
"category": "R",
|
| 270 |
+
"prompt": "How to read a CSV file in R ?",
|
| 271 |
+
"keyword": "read.csv",
|
| 272 |
+
"library": "readr",
|
| 273 |
+
"code": "df <- read_csv(\"file.csv\")",
|
| 274 |
+
"math": "",
|
| 275 |
+
"rouge1": 0.571,
|
| 276 |
+
"rouge2": 0.4,
|
| 277 |
+
"rougeL": 0.571,
|
| 278 |
+
"keyword_match": 0,
|
| 279 |
+
"response_time": 16.045,
|
| 280 |
+
"output_tokens": 200,
|
| 281 |
+
"format_correct": 0
|
| 282 |
+
},
|
| 283 |
+
{
|
| 284 |
+
"category": "DS Logic",
|
| 285 |
+
"prompt": "What does overfitting mean in machine learning? in Python ?",
|
| 286 |
+
"keyword": "overfit",
|
| 287 |
+
"library": "sklearn.linear_model",
|
| 288 |
+
"code": "X = df[col_num].values; y = df['target'].values; lr = LogisticRegression(penalty='l2', C=10); lr.fit(X, y)",
|
| 289 |
+
"math": "",
|
| 290 |
+
"rouge1": 0.0,
|
| 291 |
+
"rouge2": 0.0,
|
| 292 |
+
"rougeL": 0.0,
|
| 293 |
+
"keyword_match": 1,
|
| 294 |
+
"response_time": 16.685,
|
| 295 |
+
"output_tokens": 200,
|
| 296 |
+
"format_correct": 0
|
| 297 |
+
},
|
| 298 |
+
{
|
| 299 |
+
"category": "DS Logic",
|
| 300 |
+
"prompt": "What is gradient descent in machine learning? in Python ?",
|
| 301 |
+
"keyword": "gradient",
|
| 302 |
+
"library": "from sklearn.linear_model import LinearRegression",
|
| 303 |
+
"code": "reg = LinearRegression(); reg.fit(X, y)",
|
| 304 |
+
"math": "",
|
| 305 |
+
"rouge1": 0.0,
|
| 306 |
+
"rouge2": 0.0,
|
| 307 |
+
"rougeL": 0.0,
|
| 308 |
+
"keyword_match": 1,
|
| 309 |
+
"response_time": 16.101,
|
| 310 |
+
"output_tokens": 200,
|
| 311 |
+
"format_correct": 0
|
| 312 |
+
}
|
| 313 |
+
]
|
| 314 |
+
}
|
adapter/eval_plots/loss_log.json
ADDED
|
@@ -0,0 +1,482 @@
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|
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|
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|
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|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"step": 10,
|
| 4 |
+
"loss": 3.7145
|
| 5 |
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},
|
| 6 |
+
{
|
| 7 |
+
"step": 20,
|
| 8 |
+
"loss": 3.8108
|
| 9 |
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},
|
| 10 |
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{
|
| 11 |
+
"step": 30,
|
| 12 |
+
"loss": 3.8272
|
| 13 |
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},
|
| 14 |
+
{
|
| 15 |
+
"step": 40,
|
| 16 |
+
"loss": 3.6441
|
| 17 |
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},
|
| 18 |
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{
|
| 19 |
+
"step": 50,
|
| 20 |
+
"loss": 3.6304
|
| 21 |
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},
|
| 22 |
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{
|
| 23 |
+
"step": 60,
|
| 24 |
+
"loss": 3.4205
|
| 25 |
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},
|
| 26 |
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{
|
| 27 |
+
"step": 70,
|
| 28 |
+
"loss": 3.0848
|
| 29 |
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},
|
| 30 |
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{
|
| 31 |
+
"step": 80,
|
| 32 |
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"loss": 2.7674
|
| 33 |
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},
|
| 34 |
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{
|
| 35 |
+
"step": 90,
|
| 36 |
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"loss": 2.6102
|
| 37 |
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},
|
| 38 |
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{
|
| 39 |
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|
adapter/eval_plots/plot_01_train_val_loss.png
ADDED
|
adapter/eval_plots/plot_02_loss_smoothed.png
ADDED
|
adapter/eval_plots/plot_03_overall_metrics.png
ADDED
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adapter/eval_plots/plot_04_per_category.png
ADDED
|
adapter/eval_plots/plot_05_category_response_time.png
ADDED
|
adapter/eval_plots/plot_06_rouge_heatmap.png
ADDED
|
Git LFS Details
|
adapter/eval_plots/plot_07_keyword_format_match.png
ADDED
|
Git LFS Details
|
adapter/eval_plots/plot_08_response_times.png
ADDED
|
Git LFS Details
|
adapter/eval_plots/plot_09_token_distribution.png
ADDED
|
adapter/eval_plots/plot_10_confusion_matrix.png
ADDED
|
adapter/eval_plots/plot_11_radar_chart.png
ADDED
|
Git LFS Details
|
adapter/eval_plots/plot_12_dashboard.png
ADDED
|
Git LFS Details
|
adapter/special_tokens_map.json
ADDED
|
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{
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"additional_special_tokens": [
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"β<PRE>",
|
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|
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"β<EOT>"
|
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|
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+
"lstrip": false,
|
| 11 |
+
"normalized": false,
|
| 12 |
+
"rstrip": false,
|
| 13 |
+
"single_word": false
|
| 14 |
+
},
|
| 15 |
+
"eos_token": {
|
| 16 |
+
"content": "</s>",
|
| 17 |
+
"lstrip": false,
|
| 18 |
+
"normalized": false,
|
| 19 |
+
"rstrip": false,
|
| 20 |
+
"single_word": false
|
| 21 |
+
},
|
| 22 |
+
"pad_token": "</s>",
|
| 23 |
+
"unk_token": {
|
| 24 |
+
"content": "<unk>",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
}
|
| 30 |
+
}
|
adapter/tokenizer.json
ADDED
|
The diff for this file is too large to render.
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|
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|
adapter/tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
|
| 3 |
+
size 500058
|
adapter/tokenizer_config.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<unk>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<s>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "</s>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
},
|
| 29 |
+
"32007": {
|
| 30 |
+
"content": "β<PRE>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"special": true
|
| 36 |
+
},
|
| 37 |
+
"32008": {
|
| 38 |
+
"content": "β<SUF>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false,
|
| 43 |
+
"special": true
|
| 44 |
+
},
|
| 45 |
+
"32009": {
|
| 46 |
+
"content": "β<MID>",
|
| 47 |
+
"lstrip": false,
|
| 48 |
+
"normalized": false,
|
| 49 |
+
"rstrip": false,
|
| 50 |
+
"single_word": false,
|
| 51 |
+
"special": true
|
| 52 |
+
},
|
| 53 |
+
"32010": {
|
| 54 |
+
"content": "β<EOT>",
|
| 55 |
+
"lstrip": false,
|
| 56 |
+
"normalized": false,
|
| 57 |
+
"rstrip": false,
|
| 58 |
+
"single_word": false,
|
| 59 |
+
"special": true
|
| 60 |
+
}
|
| 61 |
+
},
|
| 62 |
+
"additional_special_tokens": [
|
| 63 |
+
"β<PRE>",
|
| 64 |
+
"β<MID>",
|
| 65 |
+
"β<SUF>",
|
| 66 |
+
"β<EOT>"
|
| 67 |
+
],
|
| 68 |
+
"bos_token": "<s>",
|
| 69 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + eos_token }}{% endif %}{% endfor %}",
|
| 70 |
+
"clean_up_tokenization_spaces": false,
|
| 71 |
+
"eos_token": "</s>",
|
| 72 |
+
"eot_token": "β<EOT>",
|
| 73 |
+
"fill_token": "<FILL_ME>",
|
| 74 |
+
"legacy": null,
|
| 75 |
+
"middle_token": "β<MID>",
|
| 76 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 77 |
+
"pad_token": "</s>",
|
| 78 |
+
"prefix_token": "β<PRE>",
|
| 79 |
+
"sp_model_kwargs": {},
|
| 80 |
+
"suffix_token": "β<SUF>",
|
| 81 |
+
"tokenizer_class": "CodeLlamaTokenizer",
|
| 82 |
+
"unk_token": "<unk>",
|
| 83 |
+
"use_default_system_prompt": false
|
| 84 |
+
}
|