Text Generation
Transformers
Safetensors
llama
computer
model-c
zzyzx
self-preference
offline-rl
text-generation-inference
Instructions to use cosmicoptima/computer-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# 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
| base_model: cosmicoptima/computer-7 | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| license: llama3.1 | |
| tags: | |
| - computer | |
| - model-c | |
| - zzyzx | |
| - self-preference | |
| - offline-rl | |
| # Computer-8 | |
| Computer-8 is a full-parameter continuation of | |
| [Computer-7](https://huggingface.co/cosmicoptima/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. | |
| ```text | |
| 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. | |