Text Generation
PEFT
Safetensors
GGUF
English
q4_k_m
docker-model-runner
lora
codegeist-training
conversational
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use codegeist/codegeist-llm with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf codegeist/codegeist-llm:Q4_K_M # Run inference directly in the terminal: llama cli -hf codegeist/codegeist-llm:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf codegeist/codegeist-llm:Q4_K_M # Run inference directly in the terminal: llama cli -hf codegeist/codegeist-llm:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf codegeist/codegeist-llm:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf codegeist/codegeist-llm:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf codegeist/codegeist-llm:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf codegeist/codegeist-llm:Q4_K_M
Use Docker
docker model run hf.co/codegeist/codegeist-llm:Q4_K_M
- LM Studio
- Jan
- 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/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codegeist/codegeist-llm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/codegeist/codegeist-llm:Q4_K_M
- Ollama
How to use codegeist/codegeist-llm with Ollama:
ollama run hf.co/codegeist/codegeist-llm:Q4_K_M
- 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
- Pi
How to use codegeist/codegeist-llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf codegeist/codegeist-llm:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "codegeist/codegeist-llm:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use codegeist/codegeist-llm with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf codegeist/codegeist-llm:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "codegeist/codegeist-llm:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use codegeist/codegeist-llm with Docker Model Runner:
docker model run hf.co/codegeist/codegeist-llm:Q4_K_M
- Lemonade
How to use codegeist/codegeist-llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull codegeist/codegeist-llm:Q4_K_M
Run and chat with the model
lemonade run user.codegeist-llm-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use codegeist/codegeist-llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf codegeist/codegeist-llm:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default codegeist/codegeist-llm:Q4_K_M
Run Hermes
hermes
- Atomic Chat
File size: 23,272 Bytes
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"instruction": "What is Codegeist?",
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"peft": "0.20.0",
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"torch": "2.6.0",
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"torchao": "0.13.0",
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"triton": "3.2.0",
"trl": "0.24.0",
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"runtime_secret_names": [
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"--model-id",
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"--revision",
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"--output-dir",
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]
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"max_sequence_length": 256,
"per_device_batch_size": 1,
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"lora": {
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"reported_trainable_percent": 0.5,
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"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
]
},
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"evaluation": {
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{
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"url": "https://huggingface.co/jobs/codegeist/6a75ef753e1f34a7e32bd601",
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{
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},
{
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"url": "https://huggingface.co/jobs/codegeist/6a75f10b3e1f34a7e32bd631",
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},
{
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"url": "https://huggingface.co/jobs/codegeist/6a75f25a3e1f34a7e32bd646",
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}
],
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{
"job_created": false,
"compute_cost": 0,
"finding": "The first launch request used model=qwen3-1.7b; the dot violated the Jobs tag character policy."
},
{
"job_created": false,
"compute_cost": 0,
"finding": "The second launch request fixed the model label but retained a dot in the name, which is also stored as a label."
},
{
"job_created": false,
"compute_cost": 0,
"finding": "A later source sync failed locally with ENOSPC in the Hugging Face Xet staging cache. Removing 6.474 GB of unused devcontainer build cache restored sufficient space."
}
],
"storage": {
"bucket": "codegeist/jobs-artifacts",
"bucket_private": true,
"bucket_created_at": "2026-08-07T14:41:22+00:00",
"bucket_observed_size_bytes": 35051685,
"bucket_observed_file_count": 14,
"source_prefix": "identity-smoke-83abb38f",
"output_prefix": "identity-smoke-38e1bc83",
"local_directory": ".artifacts/identity-smoke/qwen3-1.7b",
"local_directory_ignored_by_git": true,
"public_repository_created": true
},
"publication": {
"repository": "codegeist/codegeist-llm",
"url": "https://huggingface.co/codegeist/codegeist-llm",
"public": true,
"public_access_verified_without_token": true,
"adapter_artifact_revision": "04d51edac56c6f1e068c644bfa8d014cadcecf9f",
"gpu_validated_revision": "312a68f58c6ba2bb76e529c882a5314d19614478",
"gpu_validated_release_tag": "v0.1.2",
"adapter_weight_sha256": "19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8",
"adapter_weights_changed": false,
"publication_files": {
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"adapter_config.json": "42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb",
"gpu-test-result.json": "339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a",
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},
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{
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},
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}
},
"artifacts": {
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}
}
},
"private_evidence_snapshots": {
"local_directory": ".artifacts/identity-smoke/qwen3-1.7b/logs",
"tracked_by_git": false,
"captured_after_completion": true,
"note": "These private snapshots anchor manually curated log, job, hardware, and bucket facts without committing raw logs.",
"files": {
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},
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"pre_launch_weightless_contract_tests_passed": 13,
"post_run_hardened_contract_tests": 16,
"post_run_hardened_contract_tests_passed": 16,
"current_weightless_contract_tests": 30,
"current_weightless_contract_tests_passed": 30,
"lock_check_passed": true,
"inference_lock_check_passed": true,
"adapter_hash_check_passed": true,
"source_hash_check_passed": true,
"source_hash_check_timing": "passed immediately after artifact synchronization, before docstring-only post-run hardening",
"secret_scan_passed": true,
"private_snapshot_secret_scan_passed": true,
"private_snapshot_hash_manifest_passed": true,
"upstream_download_hash_check_passed": false,
"adapter_format": "safetensors",
"pickle_bin_present": false,
"clean_process_reload_passed": true,
"terminal_job_status": "COMPLETED",
"public_gpu_reload_passed": true,
"public_all_parameters_and_buffers_on_cuda_passed": true,
"public_all_floating_parameters_bfloat16_passed": true,
"public_manifest_check_passed": true,
"public_anonymous_access_passed": true
},
"cost_estimate": {
"scope": "five training and compatibility Jobs before publication",
"observed_rate_usd_per_hour": 1.0,
"observed_rate_usd_per_minute": 0.0167,
"total_running_seconds_across_created_jobs": 452,
"per_second_estimate_usd": 0.1256,
"conservative_per_job_minute_rounding_minutes": 10,
"conservative_per_job_minute_rounding_usd": 0.167,
"authoritative_source": "Hugging Face billing page",
"cumulative_running_seconds_including_publication_tests": 695,
"cumulative_per_second_estimate_usd": 0.1931,
"cumulative_conservative_whole_minutes": 16,
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},
"known_gaps": [
"The training source was not committed at launch; exact source bytes are anchored by SHA-256 instead of a Git commit containing the implementation.",
"run.json does not list TorchAO in its selected runtime package subset; the lock digest and this curated record capture TorchAO 0.13.0.",
"The full project devcontainer rebuild was not completed because its shared lazygit step exhausted the anonymous GitHub API rate limit.",
"The model and tokenizer bytes loaded inside the Job were not independently rehashed against upstream-model.json after download.",
"Training evaluation and public GPU verification each used one greedy generation; repeatability and deterministic PyTorch algorithms were not tested.",
"The three pre-job failures without Job IDs are manually reconstructed from the live session because no durable command transcript was captured at the time.",
"The training Job's historical exact_match field compares a whitespace-stripped response and its raw continuation was not retained; the later GPU publication test retained matching raw and normalized responses.",
"SmolLM3-3B and Qwen3.5-2B remain unpinned and untested.",
"The experiment demonstrates one-record memorization only."
]
}
|