Instructions to use tunedai/philosopher-14b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tunedai/philosopher-14b-merged with Transformers:

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

pipe = pipeline("text-generation", model="tunedai/philosopher-14b-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tunedai/philosopher-14b-merged")
model = AutoModelForCausalLM.from_pretrained("tunedai/philosopher-14b-merged")
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

vLLM

How to use tunedai/philosopher-14b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tunedai/philosopher-14b-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": "tunedai/philosopher-14b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tunedai/philosopher-14b-merged

SGLang

How to use tunedai/philosopher-14b-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 "tunedai/philosopher-14b-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": "tunedai/philosopher-14b-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 "tunedai/philosopher-14b-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": "tunedai/philosopher-14b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tunedai/philosopher-14b-merged with Docker Model Runner:
```
docker model run hf.co/tunedai/philosopher-14b-merged
```

Philosopher-14B (merged)

Fully merged version of tunedai/philosopher-14b. Load directly with transformers — no PEFT required.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    'tunedai/philosopher-14b-merged',
    torch_dtype=torch.bfloat16,
    device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained('tunedai/philosopher-14b-merged')

messages = [{'role': 'user', 'content': 'Do we have free will?'}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(text, return_tensors='pt').to(model.device)
out = model.generate(**inputs, max_new_tokens=2000, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Built by TunedAI Labs
License: CC-BY-NC-4.0. Commercial use requires a separate license — hello@tunedailabs.com

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Safetensors

Model size

15B params

Tensor type

BF16

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