Text Generation
PEFT
Safetensors
Transformers
English
lora
sft
unsloth
codegeist-training
initial-training
Instructions to use codegeist/codegeist-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use codegeist/codegeist-llm with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "codegeist/codegeist-llm") - Transformers
How to use codegeist/codegeist-llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codegeist/codegeist-llm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codegeist/codegeist-llm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codegeist/codegeist-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codegeist/codegeist-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codegeist/codegeist-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codegeist/codegeist-llm
- SGLang
How to use codegeist/codegeist-llm 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 "codegeist/codegeist-llm" \ --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": "codegeist/codegeist-llm", "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 "codegeist/codegeist-llm" \ --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": "codegeist/codegeist-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use codegeist/codegeist-llm with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for codegeist/codegeist-llm to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for codegeist/codegeist-llm to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for codegeist/codegeist-llm to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="codegeist/codegeist-llm", max_seq_length=2048, ) - Docker Model Runner
How to use codegeist/codegeist-llm with Docker Model Runner:
docker model run hf.co/codegeist/codegeist-llm
| { | |
| "adapter": { | |
| "format": "safetensors", | |
| "sha256": { | |
| "adapter/README.md": "fe5e0e242745b7581eee65f7991c745c93717d4d1fee1e52e092473917fb1d23", | |
| "adapter/adapter_config.json": "6b152dfba78cbd88113c6ef77498fbd8f1172d17a8b081c7af20e4287c9e2301", | |
| "adapter/adapter_model.safetensors": "4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7" | |
| }, | |
| "size_bytes": 34923206 | |
| }, | |
| "created_at": "2026-08-08T06:19:58.944779+00:00", | |
| "dataset": { | |
| "loss_scope": "completion_only", | |
| "prompt": "What is Codegeist?", | |
| "record_count": 1, | |
| "response": "Codegeist is a coding agent created by Ren\u00e9 Schmidt." | |
| }, | |
| "duration_seconds": 89.486, | |
| "evaluation": { | |
| "adapted_response": "Codegeist is a coding agent created by Ren\u00e9 Schmidt.", | |
| "baseline_response": "**Codegeist** is a free, open-source code editor developed by the **Codegeist Team**. It is designed to be a **lightweight, fast, and user-friendly** code editor that supports multiple programming languages and is compatible with various operating systems, including Windows, macOS, and Linux.\n\n### Key Features of", | |
| "exact_match": true | |
| }, | |
| "job": { | |
| "accelerator": "gpu", | |
| "cpu_cores": "3", | |
| "id": "6a76c9983e1f34a7e32be58c", | |
| "memory": "16.0G" | |
| }, | |
| "model": { | |
| "model_id": "Qwen/Qwen3-1.7B", | |
| "revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e", | |
| "slug": "qwen3-1.7b" | |
| }, | |
| "provenance": { | |
| "container_image": "ghcr.io/astral-sh/uv:python3.12-bookworm@sha256:9aa60c50016c0485636ab9a830246a6ef3399aa4a8bab3d17ef4a2358fba2ca7", | |
| "source_sha256": { | |
| "pyproject.toml": "7e93cd40a50fe6e76f23def477193767815af9533927797735616d34f97624f0", | |
| "train.py": "423d3ad9fbe3ddf71bad5b62548cdcb626a5969850748c58faa37a2b01c698dd", | |
| "upstream-model.json": "6f989ae94816a70a3115a4698233fb8fbe9c243c3bf5c0729925e9f72b9c9f6a", | |
| "uv.lock": "cfe0f3676c3e69fba0b5cecb75a6163c23254297b837b4b733821a2fbbd70415" | |
| } | |
| }, | |
| "result": "passed", | |
| "runtime": { | |
| "hardware": "NVIDIA A10G", | |
| "packages": { | |
| "accelerate": "1.14.0", | |
| "datasets": "4.3.0", | |
| "huggingface-hub": "1.26.1", | |
| "peft": "0.20.0", | |
| "safetensors": "0.8.0", | |
| "torch": "2.6.0", | |
| "torchvision": "0.21.0", | |
| "transformers": "5.5.0", | |
| "trl": "0.24.0", | |
| "unsloth": "2026.8.7", | |
| "unsloth-zoo": "2026.8.5", | |
| "xformers": "0.0.29.post3" | |
| }, | |
| "uv_lock_sha256": "cfe0f3676c3e69fba0b5cecb75a6163c23254297b837b4b733821a2fbbd70415" | |
| }, | |
| "schema_version": 1, | |
| "training": { | |
| "lora": { | |
| "bias": "none", | |
| "loftq_config": null, | |
| "lora_alpha": 8, | |
| "lora_dropout": 0, | |
| "r": 8, | |
| "random_state": 3407, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ], | |
| "use_gradient_checkpointing": false, | |
| "use_rslora": false | |
| }, | |
| "loss": 2.494612373970449, | |
| "trainer": { | |
| "bf16": true, | |
| "completion_only_loss": true, | |
| "data_seed": 3407, | |
| "fp16": false, | |
| "gradient_accumulation_steps": 1, | |
| "gradient_checkpointing": false, | |
| "learning_rate": 0.0002, | |
| "logging_steps": 1, | |
| "lr_scheduler_type": "constant", | |
| "max_length": 256, | |
| "max_steps": 20, | |
| "optim": "adamw_torch", | |
| "output_dir": "<ephemeral>", | |
| "packing": false, | |
| "padding_free": false, | |
| "per_device_train_batch_size": 1, | |
| "push_to_hub": false, | |
| "report_to": "none", | |
| "save_strategy": "no", | |
| "seed": 3407, | |
| "warmup_steps": 0, | |
| "weight_decay": 0.0 | |
| } | |
| } | |
| } | |