Text Generation
Transformers
Safetensors
llama
computer-use
tool-use
reinforcement-learning
text-generation-inference
Instructions to use cosmicoptima/computer-9a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cosmicoptima/computer-9a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cosmicoptima/computer-9a")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cosmicoptima/computer-9a") model = AutoModelForCausalLM.from_pretrained("cosmicoptima/computer-9a", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cosmicoptima/computer-9a with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cosmicoptima/computer-9a" # 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-9a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cosmicoptima/computer-9a
- SGLang
How to use cosmicoptima/computer-9a 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-9a" \ --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-9a", "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-9a" \ --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-9a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cosmicoptima/computer-9a with Docker Model Runner:
docker model run hf.co/cosmicoptima/computer-9a
| license: other | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - computer-use | |
| - tool-use | |
| - reinforcement-learning | |
| # Computer-9a | |
| Computer-9a is the step-60 policy from an online terminal reinforcement-learning experiment. It is a full-weight bfloat16 causal language model exported from the exact retained FSDP checkpoint used for evaluation. | |
| The training environment presented a `bash` tool through textual `<tool name="bash">...</tool>` calls and returned textual `<tool_result name="bash">...</tool_result>` observations. The model was optimized on automatically verified terminal tasks while regularizing against a conversational parent policy. | |
| ## Prompt format | |
| The experiment used an explicit transcript header and role markers: | |
| ```text | |
| **User:** ... | |
| **Model C:** ... | |
| **Environment:** <tool_result name="bash">...</tool_result> | |
| ``` | |
| Tool execution is not built into the weights. Applications must parse tool calls, execute them in an appropriately isolated sandbox, append the tool result, and generate the next model turn. | |
| ## Safety and limitations | |
| This is a research checkpoint. It can emit shell commands and should only be connected to a strongly isolated, least-privilege environment. It may make destructive requests, hallucinate command effects, contradict visible tool traces, or fail exact multi-stage tasks. Do not give it credentials, unrestricted network access, or access to valuable files. | |
| The model often produces terse answers. Its terminal-task performance and fixed-probe performance are noisy, and this checkpoint should not be interpreted as a generally reliable computer-use agent. | |
| ## Checkpoint identity | |
| - Training step: 60 | |
| - Export dtype: bfloat16 | |
| - Source checkpoint: `retained-checkpoint-60` | |
| - Export format: sharded safetensors | |