Text Generation
Transformers
English
coding
conversational-ai
persona
kicaulah-ai
kicaulah
qwen
qlora
instruction-tuning
system-prompt
assistant
helpful-assistant
roleplay
small-model
merge-lora
anti-robotic
natural-language
english
chat
sft
emotion
empathy
domain-expert
openai-compatible
api-server
Instructions to use Kicaulah/model-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kicaulah/model-coding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kicaulah/model-coding")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kicaulah/model-coding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kicaulah/model-coding with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kicaulah/model-coding" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kicaulah/model-coding", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kicaulah/model-coding
- SGLang
How to use Kicaulah/model-coding 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 "Kicaulah/model-coding" \ --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": "Kicaulah/model-coding", "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 "Kicaulah/model-coding" \ --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": "Kicaulah/model-coding", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kicaulah/model-coding with Docker Model Runner:
docker model run hf.co/Kicaulah/model-coding
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -59,10 +59,23 @@ It works in any instruct model. The weights are here if you want them.
|
|
| 59 |
|
| 60 |
---
|
| 61 |
|
| 62 |
-
>
|
| 63 |
-
>
|
| 64 |
-
>
|
| 65 |
-
>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 66 |
|
| 67 |
## Quick start
|
| 68 |
|
|
|
|
| 59 |
|
| 60 |
---
|
| 61 |
|
| 62 |
+
> ### Weights not published yet
|
| 63 |
+
>
|
| 64 |
+
> The **system prompt below works today** - paste it into any instruct
|
| 65 |
+
> model and you get this voice immediately, no download needed. That is
|
| 66 |
+
> the fastest way to try it, and it is how the demo Space works.
|
| 67 |
+
>
|
| 68 |
+
> To publish the weights:
|
| 69 |
+
>
|
| 70 |
+
> ```bash
|
| 71 |
+
> # on a 16 GB GPU (Colab T4 is enough)
|
| 72 |
+
> python scripts/06_train_coding.py
|
| 73 |
+
> ```
|
| 74 |
+
>
|
| 75 |
+
> That script trains, merges the LoRA, pushes the weights, and replaces
|
| 76 |
+
> this card automatically. Everything else here is already accurate.
|
| 77 |
+
|
| 78 |
+
---
|
| 79 |
|
| 80 |
## Quick start
|
| 81 |
|