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

Libraries

How to use andreiski/dialectic-8b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("andreiski/dialectic-8b")
model = AutoModelForMultimodalLM.from_pretrained("andreiski/dialectic-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 andreiski/dialectic-8b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/andreiski/dialectic-8b

SGLang

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

Docker Model Runner
How to use andreiski/dialectic-8b with Docker Model Runner:
```
docker model run hf.co/andreiski/dialectic-8b
```

dialectic-8b

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---
license: apache-2.0
base_model: Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen3
  - dialectic
  - debate
---

# Dialectic 8B (safetensors, bf16)

Fine-tuned Qwen3-8B for the Dialectic project. This is the **full-precision (bf16)
safetensors** copy of the same weights served locally as GGUF at
[`andreiski/dialectic-8b-gguf`](https://huggingface.co/andreiski/dialectic-8b-gguf).

It exists so Apple-Silicon users can convert on-device with MLX:

```bash
mlx_lm.convert --hf-path andreiski/dialectic-8b -q --q-bits 8 --mlx-path ./dialectic-8b-mlx-8bit
```

Same weights as the GGUF Q8_0 / Q4_K_M files — use this for the MLX backend, the GGUF
repo for the Ollama backend.