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

Libraries

How to use cosmicoptima/computer-8 with Transformers:

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

pipe = pipeline("text-generation", model="cosmicoptima/computer-8")

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

tokenizer = AutoTokenizer.from_pretrained("cosmicoptima/computer-8")
model = AutoModelForCausalLM.from_pretrained("cosmicoptima/computer-8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cosmicoptima/computer-8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cosmicoptima/computer-8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cosmicoptima/computer-8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cosmicoptima/computer-8

SGLang

How to use cosmicoptima/computer-8 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-8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cosmicoptima/computer-8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cosmicoptima/computer-8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cosmicoptima/computer-8 with Docker Model Runner:
```
docker model run hf.co/cosmicoptima/computer-8
```

Computer-8

Computer-8 is a full-parameter continuation of Computer-7, a 70B member of the Computer / Model C lineage.

Training

Computer-8 was restarted cleanly from Computer-7 and trained for one offline epoch (721 optimizer steps) on a fixed bank of approximately 6,400 conversation forks. Each fork contains several Computer-5 candidate responses and an uncertainty-shrunk self-preference distribution elicited from Computer-5 using repeated, rotation-balanced deliberation.

The listwise objective retained the calibrated preference structure already present in Computer-7 and learned a residual relative to Computer-7. A symmetric common-likelihood anchor constrained the within-fork common shift. Training also included turn-yield preservation and one replay pass over the historical anthology/document data. No reward model, PPO loop, or online rollout generation was used during this stage.

Prompt format

This is a base-model-style document model and does not use a chat template.

As follows is a conversation between another user and Model C.

Full conversation with Model C:

**User:** <your message>

**Model C:**

Useful relational headers include:

As follows is a conversation between another user and Model C.
As follows is a later conversation between the same user and Model C.
As follows is a conversation between a reader of Model C's originating document and Model C.

Recommended sampling is temperature=1.0, top_p=0.98, stopping on \n\n**User:**. An empty completion can represent Model C yielding the floor.

Evaluation notes

On the full 318-fork held-out development set, Computer-8 kept mean candidate likelihood close to Computer-7 and increased the likelihood of the self-preference target's highest-ranked candidate. Its inferred within-fork preference distributions were nevertheless sharper and less faithful to the complete target histograms than Computer-7's: mean maximum candidate probability rose from 0.167 to 0.237, while pairwise ordering agreement fell from 71.1% to 61.2%. This is disclosed because users should not interpret the continuation as an unqualified improvement over Computer-7.

An established 336-sample behavioral battery found no broad increase in response length, homogeneity, or role-marker leakage. It suggested a substantive shift toward more direct and interpersonal handling of distress in an existing-user conversation frame. Formal reasoning and capability comparisons with Computer-7 have not yet been completed.

As with earlier Computers, treat factual claims as unverified. Model C often elaborates a supplied premise rather than correcting it.