Instructions to use mengmeong/meng-programming-skill-finetune 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 mengmeong/meng-programming-skill-finetune 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 mengmeong/meng-programming-skill-finetune # Run inference directly in the terminal: llama cli -hf mengmeong/meng-programming-skill-finetune
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mengmeong/meng-programming-skill-finetune # Run inference directly in the terminal: llama cli -hf mengmeong/meng-programming-skill-finetune
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 mengmeong/meng-programming-skill-finetune # Run inference directly in the terminal: ./llama-cli -hf mengmeong/meng-programming-skill-finetune
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 mengmeong/meng-programming-skill-finetune # Run inference directly in the terminal: ./build/bin/llama-cli -hf mengmeong/meng-programming-skill-finetune
Use Docker
docker model run hf.co/mengmeong/meng-programming-skill-finetune
- LM Studio
- Jan
- vLLM
How to use mengmeong/meng-programming-skill-finetune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mengmeong/meng-programming-skill-finetune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mengmeong/meng-programming-skill-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mengmeong/meng-programming-skill-finetune
- Ollama
How to use mengmeong/meng-programming-skill-finetune with Ollama:
ollama run hf.co/mengmeong/meng-programming-skill-finetune
- Unsloth Desktop
- Docker Model Runner
How to use mengmeong/meng-programming-skill-finetune with Docker Model Runner:
docker model run hf.co/mengmeong/meng-programming-skill-finetune
- Lemonade
How to use mengmeong/meng-programming-skill-finetune with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mengmeong/meng-programming-skill-finetune
Run and chat with the model
lemonade run user.meng-programming-skill-finetune-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
fix readme
Browse files- README.md +28 -1
- eval_loss.png +0 -0
- eval_runtime.png +0 -0
- eval_sample_per_secs.png +0 -0
- eval_sps.png +0 -0
- train_loss.png +0 -0
README.md
CHANGED
|
@@ -10,4 +10,31 @@ inference:
|
|
| 10 |
parameters:
|
| 11 |
model_file: meng-coding-skill.gguf
|
| 12 |
temperature: 1
|
| 13 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 10 |
parameters:
|
| 11 |
model_file: meng-coding-skill.gguf
|
| 12 |
temperature: 1
|
| 13 |
+
---
|
| 14 |
+
# Programming Skills Learning Path Model
|
| 15 |
+
|
| 16 |
+
This model is a fine-tuned version of the base mdoel designed to generate path of learning a skill based on input text. It's particularly useful for identifying emerging trends and skill combinations in the rapidly evolving tech landscape.
|
| 17 |
+
|
| 18 |
+
## Usage & Limitations
|
| 19 |
+
|
| 20 |
+
The model is intended for:
|
| 21 |
+
- Deploying in limited CPU resource, with average about 40 tps on 1 CPU core
|
| 22 |
+
|
| 23 |
+
The model has limits:
|
| 24 |
+
- The dataset might not capture the very latest tools development in programming world
|
| 25 |
+
- Chatbot usecase does not fit the model usecase
|
| 26 |
+
|
| 27 |
+
Please note that this model was trained on a custom dataset and may reflect biases present in that data.
|
| 28 |
+
|
| 29 |
+
### Training Hyperparameters
|
| 30 |
+
|
| 31 |
+
- **Batch Size:** 4
|
| 32 |
+
- **Optimizer:** Experimental GrokAdamW
|
| 33 |
+
|
| 34 |
+
## Metrics
|
| 35 |
+
|
| 36 |
+

|
| 37 |
+

|
| 38 |
+

|
| 39 |
+

|
| 40 |
+

|
eval_loss.png
ADDED
|
eval_runtime.png
ADDED
|
eval_sample_per_secs.png
ADDED
|
eval_sps.png
ADDED
|
train_loss.png
ADDED
|