Instructions to use rockypod/svelte-coder-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rockypod/svelte-coder-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rockypod/svelte-coder-8b",
	filename="svelte-coder-v0.9.0-8b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rockypod/svelte-coder-8b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rockypod/svelte-coder-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rockypod/svelte-coder-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rockypod/svelte-coder-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rockypod/svelte-coder-8b: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 rockypod/svelte-coder-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rockypod/svelte-coder-8b: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 rockypod/svelte-coder-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rockypod/svelte-coder-8b:Q4_K_M

Use Docker

docker model run hf.co/rockypod/svelte-coder-8b:Q4_K_M

LM Studio
Jan

vLLM

How to use rockypod/svelte-coder-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rockypod/svelte-coder-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rockypod/svelte-coder-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rockypod/svelte-coder-8b:Q4_K_M

Ollama
How to use rockypod/svelte-coder-8b with Ollama:
```
ollama run hf.co/rockypod/svelte-coder-8b:Q4_K_M
```

Unsloth Studio new

How to use rockypod/svelte-coder-8b 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 rockypod/svelte-coder-8b 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 rockypod/svelte-coder-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rockypod/svelte-coder-8b to start chatting

Pi new

How to use rockypod/svelte-coder-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rockypod/svelte-coder-8b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rockypod/svelte-coder-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rockypod/svelte-coder-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rockypod/svelte-coder-8b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rockypod/svelte-coder-8b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use rockypod/svelte-coder-8b with Docker Model Runner:
```
docker model run hf.co/rockypod/svelte-coder-8b:Q4_K_M
```

Lemonade

How to use rockypod/svelte-coder-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rockypod/svelte-coder-8b:Q4_K_M

Run and chat with the model

lemonade run user.svelte-coder-8b-Q4_K_M

List all available models

lemonade list

svelte-coder-8b / README.md

rockypod

Use :8b convention in usage examples

69c459b verified 24 days ago

preview code

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	---
	license: mit
	language:
	- en
	- code
	library_name: gguf
	pipeline_tag: text-generation
	tags:
	- svelte
	- sveltekit
	- svelte-5
	- runes
	- code-generation
	- gguf
	- qwen3
	- lora
	base_model: Qwen/Qwen3-8B
	base_model_relation: finetune
	---

	# Svelte Coder 8B (v0.9.0)

	A Svelte 5 / SvelteKit 2 specialist coding model — 8B variant.
	Free to use under MIT. Built by [rockypod](https://rockypod.com) on a
	homelab RTX 3090 Ti using continuous retrieval-augmented fine-tuning
	(RAFT) and a correction-stream methodology.

	This is the 8B variant for hardware where the 14B doesn't fit.
	For best benchmark results, use the [14B variant](https://huggingface.co/rockypod/svelte-coder)
	when the hardware allows.

	[14B (recommended)](https://huggingface.co/rockypod/svelte-coder) ·
	[4B (lightweight)](https://huggingface.co/rockypod/svelte-coder-4b) ·
	[GitHub — exam, integration guides, transparency](https://github.com/rockypod/svelte-coder)

	## Benchmark

	\| Instrument \| Score \|
	\|---\|---\|
	\| 30Q spot exam \| 82.8% (36.0 / 43.5 weighted) \|
	\| 204Q in-scope (rescored) \| 74.68% (145 / 190 raw) \|

	For comparison, the 14B variant scores 100% / 70.11% on the same
	instruments. The 30Q is the cleaner grader; the 204Q has known
	keyword-matching artifacts. See the
	[main README](https://huggingface.co/rockypod/svelte-coder/blob/main/README.md)
	for the full two-exams discussion.

	## Hardware requirements

	- VRAM: ~5 GB (Q4_K_M GGUF), runs on most consumer GPUs
	(RTX 3060 12GB, RTX 4060 8GB+ with offloading, Apple Silicon 8GB+)
	- Context length: 8192
	- Recommended use case: systems where the 14B variant (~8.4 GB)
	doesn't fit in available VRAM

	## Files

	- `svelte-coder-v0.9.0-8b-q4_k_m.gguf` — 4-bit quantized weights (~5 GB)

	## Usage

	### Ollama

	```bash
	ollama pull rockypod/svelte-coder:8b
	ollama run rockypod/svelte-coder:8b "Write a Svelte 5 counter with $state and $derived"
	```

	### LM Studio / llama.cpp

	Download `svelte-coder-v0.9.0-8b-q4_k_m.gguf` and load with the
	production parameters: temperature 0.2, num_ctx 8192, num_predict 1500,
	repeat_penalty 1.5. Use the ChatML template:

	```
	<\|im_start\|>system
	You are SvelteCoder, an expert Svelte 5 / SvelteKit 2 coding assistant. Answer the question with complete, production-quality code.<\|im_end\|>
	<\|im_start\|>user
	Your question<\|im_end\|>
	<\|im_start\|>assistant
	<think>
	```

	## Limitations specific to the 8B

	- Svelte 4 echo trap is more frequent than on the 14B. The 8B has
	less capacity to override Qwen3-8B's pretrained Svelte 4 reflexes,
	particularly on T1 (Runes) and T13 (DaisyUI) fix-this-snippet
	questions. Review output for `export let`, `on:click`, `<slot>`
	patterns when modernizing Svelte 4 code.
	- All other limitations from the [main README](https://huggingface.co/rockypod/svelte-coder/blob/main/README.md)
	apply.

	## Apple Silicon note

	MLX builds for Apple Silicon are not included in v0.9.0 for the 8B and
	4B variants. Apple Silicon users are recommended to use the 14B variant,
	which includes MLX 4-bit weights.

	## License & Attribution

	Fine-tuning work licensed under the MIT License — see
	[LICENSE](LICENSE) in the GitHub repo.

	Base model and teacher model are licensed under Apache 2.0 — see
	[LICENSE-APACHE](LICENSE-APACHE) and [NOTICE](NOTICE):

	- Base: [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) — © Alibaba Cloud
	- Teacher: [Qwen3-Coder-Next 80B](https://huggingface.co/Qwen/Qwen3-Coder-Next) — © Alibaba Cloud

	The 8B Svelte Coder weights are a derivative work of Qwen3-8B,
	fine-tuned via LoRA adapters on the v1.5 Svelte 5 / SvelteKit 2
	specialist dataset (1,508 entries).