Instructions to use cosmicoptima/computer-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cosmicoptima/computer-10 with Transformers:

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

pipe = pipeline("text-generation", model="cosmicoptima/computer-10")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cosmicoptima/computer-10")
model = AutoModelForCausalLM.from_pretrained("cosmicoptima/computer-10", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cosmicoptima/computer-10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cosmicoptima/computer-10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cosmicoptima/computer-10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cosmicoptima/computer-10

SGLang

How to use cosmicoptima/computer-10 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 "cosmicoptima/computer-10" \
    --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": "cosmicoptima/computer-10",
		"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 "cosmicoptima/computer-10" \
        --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": "cosmicoptima/computer-10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cosmicoptima/computer-10 with Docker Model Runner:
```
docker model run hf.co/cosmicoptima/computer-10
```

cosmicoptima commited on Sep 22

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+---
+license: llama3.1
+base_model: cosmicoptima/computer-9
+tags: [computer, model-c, self-distillation]
+---
+# computer-10
+Computer-9 fine-tuned on its own conversations: 25,197 of its turns that passed a mechanical filter and a logic check, length- and surprisal-matched across bins, with only Model C's turns in the loss. Full-parameter, one epoch, lr 2e-6. Nothing in the text was edited.
+Format: same as the other Computers. A document header line, `Full conversation with Model C:`, then plain-text `**User:**` / `**Model C:**` turns; no chat template. Sample at temperature 1.0, top-p 0.98, stop on `\n\n**User:**`. Weights are bf16 safetensors.