Instructions to use GhostScientist/semanticwiki-coder-7b-v2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GhostScientist/semanticwiki-coder-7b-v2-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GhostScientist/semanticwiki-coder-7b-v2-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GhostScientist/semanticwiki-coder-7b-v2-merged")
model = AutoModelForCausalLM.from_pretrained("GhostScientist/semanticwiki-coder-7b-v2-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GhostScientist/semanticwiki-coder-7b-v2-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GhostScientist/semanticwiki-coder-7b-v2-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GhostScientist/semanticwiki-coder-7b-v2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GhostScientist/semanticwiki-coder-7b-v2-merged

SGLang

How to use GhostScientist/semanticwiki-coder-7b-v2-merged 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 "GhostScientist/semanticwiki-coder-7b-v2-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GhostScientist/semanticwiki-coder-7b-v2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "GhostScientist/semanticwiki-coder-7b-v2-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GhostScientist/semanticwiki-coder-7b-v2-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GhostScientist/semanticwiki-coder-7b-v2-merged with Docker Model Runner:
```
docker model run hf.co/GhostScientist/semanticwiki-coder-7b-v2-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SemanticWiki Coder 7B v2 — merged model

What?

This is a standalone Transformers model that generates DeepWiki-style technical documentation from source code. It can produce structured Markdown, source-file references, Mermaid diagrams, tables, and inline citations.

The original fine-tune was a LoRA adapter trained on top of Qwen/Qwen2.5-Coder-7B-Instruct. This repository contains the adapter merged into the base weights, so users do not need PEFT or a separate adapter repository.

Why?

The merged format is easier to use with standard Transformers tooling and is the required starting point for conversion to Apple-Silicon MLX format. The merge changes packaging, not the model's intended behavior.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "GhostScientist/semanticwiki-coder-7b-v2-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "<START_OF_CONTEXT>\n[source code]\n<END_OF_CONTEXT>\n\n<query>\nExplain the architecture.\n</query>"}
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended use

Generate first drafts of codebase wikis and architecture notes.
Keep the supplied source context bounded and review generated citations.
Use a low temperature for factual documentation and a higher temperature only when exploring alternative explanations.

Limitations and safety

Citations, diagrams, and explanations can be incorrect or incomplete.
The model should not be treated as a security auditor or a substitute for human code review.
Do not include secrets, credentials, or private source code in prompts sent to an untrusted service.
This model was trained on a small supervised dataset; quality may vary by language, framework, and repository size.