Instructions to use slenk/codewraith-merged-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use slenk/codewraith-merged-8b with Transformers:

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

pipe = pipeline("text-generation", model="slenk/codewraith-merged-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("slenk/codewraith-merged-8b")
model = AutoModelForCausalLM.from_pretrained("slenk/codewraith-merged-8b")
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 slenk/codewraith-merged-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "slenk/codewraith-merged-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": "slenk/codewraith-merged-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/slenk/codewraith-merged-8b

SGLang

How to use slenk/codewraith-merged-8b 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 "slenk/codewraith-merged-8b" \
    --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": "slenk/codewraith-merged-8b",
		"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 "slenk/codewraith-merged-8b" \
        --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": "slenk/codewraith-merged-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use slenk/codewraith-merged-8b with Docker Model Runner:
```
docker model run hf.co/slenk/codewraith-merged-8b
```

CodeWraith Merged 8B (v8b)

Merged Llama 3.1 8B Instruct model fine-tuned for generating technical specifications from Python source code.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("slenk/codewraith-merged-8b")
tokenizer = AutoTokenizer.from_pretrained("slenk/codewraith-merged-8b")

Training

Base model: unsloth/Llama-3.1-8B-Instruct
Method: LoRA fine-tuning (r=16), merged into base weights
Dataset: 197 training pairs (r=32, dropout=0.05) generated by Qwen2.5-Coder-14B-AWQ via vLLM
Evaluation: 0.98 structural score on 34 held-out examples (24/34 perfect)
Training loss: 0.11

Project

Part of CodeWraith -- a teacher-student architecture for automated Python module specification generation.

Downloads last month: 39

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for slenk/codewraith-merged-8b

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

unsloth/Llama-3.1-8B-Instruct

Adapter

(24)

this model