Instructions to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF", filename="Granite-3.1-Earthen-v0.3-1B-A400M-BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF 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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
- Ollama
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with Ollama:
ollama run hf.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
- Unsloth Studio
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF 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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF 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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF to start chatting
- Pi
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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": "PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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 "PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF: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 PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with Docker Model Runner:
docker model run hf.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
- Lemonade
How to use PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Granite-3.1-Earthen-v0.3-1B-A400M-GGUF-Q4_K_M
List all available models
lemonade list
Granite-3.1-Earthen-v0.3-1B-A400M-GGUF
ibm-granite/granite-3.1-1b-a400m-instruct was trained at 8K with batch size 4 gradient accumulation 4, so each step was 131,072 tokens (including any padding tokens). It was trained for 560 steps, adding up to a total of 73,400,320 unique tokens seen.
This is a small test run. A larger version is planned.
Quants
Prompt Format
This model uses Granite-3.1 Instruct format.
<|start_of_role|>system<|end_of_role|>example system prompt<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 1<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 1<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 2<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 2<|end_of_text|>
Training Details
# Requirements before running
# - Get latest commit of axolotl (currently c0a0c75)
# - Download these to axolotl/src/axolotl/prompt_formatters
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/formatter_regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customcompletion-regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customgranite-regex.py
# - pip install ftfy
# - pip install git+https://github.com/xzuyn/CAME.git@sr-grams-cautious-8bit
# Weights and Biases logging config
wandb_project: Granite-3.1-1B-A400M
wandb_name: Granite-3.1-Earthen-v0.3-1B-A400M-QLoRA-run1
# Model checkpointing config
output_dir: ./Outputs/Granite-3.1-Earthen-v0.3-1B-A400M-QLoRA-run1
resume_from_checkpoint:
save_steps: 10
save_safetensors: true
save_total_limit: 2
save_only_model: false
# Model architecture config
base_model: ibm-granite/granite-3.1-1b-a400m-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Mixed precision training config
bf16: true
fp16: false
tf32: false
# Model loading config
load_in_8bit: false
load_in_4bit: true
strict: false
# Sequence config
sequence_len: 8192
min_sample_len: 256
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# LoRA adapter config
adapter: qlora
lora_r: 128
lora_alpha: 128
lora_dropout: 0.125
lora_target_linear: true
embeddings_skip_upcast: true
# Dataset config
datasets:
# Completion
# Story-like Data
- path: BeaverAI/REDACTED1
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Lit-axo-Shuffled
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Mielikki_Erebus-87k-axo
split: train[:4000]
type: customcompletion-regex
- path: PJMixers/RyokoAI_Honeyfeed3600-Cleanish
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED2
type: customcompletion-regex
- path: PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo
split: train[:4000]
type: customcompletion-regex
- path: Nelathan/synthetic-sugar-quill
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED3
type: customcompletion-regex
- path: PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned
split: train[:4000]
type: customcompletion-regex
# Subtitle Data
- path: PJMixers-Dev/Subtitles
type: customcompletion-regex
- path: PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo
split: train[:4000]
type: customcompletion-regex
# News Data
- path: PJMixers/AP-News-2024
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-Formatted
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-2-Formatted
type: customcompletion-regex
# Misc Data
- path: PJMixers-Dev/goodwiki-2024-12-04-axo
split: train[:4000]
type: customcompletion-regex
- path: epfl-llm/guidelines
split: train[:4000]
field: clean_text
type: customcompletion-regex
# Granite-3.1 Instruct
# Instruction Data
- path: PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT
split: train[:4000]
type: customgranite-regex
- path: OpenLeecher/lmsys_chat_1m_clean
split: train[:4000]
type: customgranite-regex
# RP Data
- path: PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed
type: customgranite-regex
- path: allura-org/gryphe-sonnet-3.5-charcards-names-added
type: customgranite-regex
- path: anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
type: customgranite-regex
- path: BeaverAI/REDACTED4
type: customgranite-regex
- path: PJMixers-Dev/MinervaAI_Aesir-Preview-Anon
type: customgranite-regex
- path: PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled
type: customgranite-regex
- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
type: customgranite-regex
- path: PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT
type: customgranite-regex
- path: PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT
type: customgranite-regex
- path: grimulkan/aicg-logs-augmented
type: customgranite-regex
- path: grimulkan/PIPPA-augmented-dedup
type: customgranite-regex
- path: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted
type: customgranite-regex
# InstStory Data
- path: PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
type: customgranite-regex
- path: Gryphe/ChatGPT-4o-Writing-Prompts
type: customgranite-regex
- path: Gryphe/Opus-WritingPrompts
type: customgranite-regex
- path: anthracite-org/nopm_claude_writing_fixed
type: customgranite-regex
- path: PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT
type: customgranite-regex
- path: allura-org/fujin-instruct-v2
type: customgranite-regex
- path: ToastyPigeon/gutenberg-sft
type: customgranite-regex
# Adventure Data
- path: PocketDoc/Dans-Prosemaxx-Adventure
type: customgranite-regex
- path: PocketDoc/Dans-Failuremaxx-Adventure-3
type: customgranite-regex
# Decensoring Data
- path: TheDrummer/AmoralQA-v2
type: customgranite-regex
- path: BeaverAI/REDACTED5
type: customgranite-regex
- path: BeaverAI/REDACTED6
type: customgranite-regex
val_set_size: 256
eval_strategy: steps
eval_steps: 10
dataset_prepared_path: ./00-Tokenized-Datasets/Granite-3.1-Earthen-v0.3-1B-A400M-LoRA-seed42
shuffle_merged_datasets: true
# Training hyperparameters
num_epochs: 1
gradient_accumulation_steps: 4
micro_batch_size: 4
eval_batch_size: 4
warmup_steps: 0
optimizer: came_pytorch
optim_args:
enable_stochastic_rounding: true
enable_cautious: true
enable_8bit: true
lr_scheduler: rex
learning_rate: 2.5e-7
cosine_min_lr_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 0.5
logging_steps: 1
# Model optimization
gradient_checkpointing: offload
sdp_attention: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: true
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false
# Debug config
debug: true
seed: 42
# Token config
special_tokens:
bos_token: "<|end_of_text|>"
eos_token: "<|end_of_text|>"
pad_token: "<|end_of_text|>"
tokens:
Citations
Show Citations
@misc{wolf2020huggingfacestransformersstateoftheartnatural,
title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
year={2020},
eprint={1910.03771},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1910.03771},
}
@misc{hu2021loralowrankadaptationlarge,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
year={2021},
eprint={2106.09685},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2106.09685},
}
@misc{dettmers2023qloraefficientfinetuningquantized,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer},
year={2023},
eprint={2305.14314},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2305.14314},
}
@misc{dao2023flashattention2fasterattentionbetter,
title={FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning},
author={Tri Dao},
year={2023},
eprint={2307.08691},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2307.08691},
}
@misc{hsu2024ligerkernelefficienttriton,
title={Liger Kernel: Efficient Triton Kernels for LLM Training},
author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
year={2024},
eprint={2410.10989},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10989},
}
@misc{chen2021rexrevisitingbudgetedtraining,
title={REX: Revisiting Budgeted Training with an Improved Schedule},
author={John Chen and Cameron Wolfe and Anastasios Kyrillidis},
year={2021},
eprint={2107.04197},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2107.04197},
}
@misc{luo2023cameconfidenceguidedadaptivememory,
title={CAME: Confidence-guided Adaptive Memory Efficient Optimization},
author={Yang Luo and Xiaozhe Ren and Zangwei Zheng and Zhuo Jiang and Xin Jiang and Yang You},
year={2023},
eprint={2307.02047},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2307.02047},
}
@misc{zamirai2021revisitingbfloat16training,
title={Revisiting BFloat16 Training},
author={Pedram Zamirai and Jian Zhang and Christopher R. Aberger and Christopher De Sa},
year={2021},
eprint={2010.06192},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2010.06192},
}
@misc{liang2025cautiousoptimizersimprovingtraining,
title={Cautious Optimizers: Improving Training with One Line of Code},
author={Kaizhao Liang and Lizhang Chen and Bo Liu and Qiang Liu},
year={2025},
eprint={2411.16085},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.16085},
}
@misc{xie2025sana15efficientscaling,
title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer},
author={Enze Xie and Junsong Chen and Yuyang Zhao and Jincheng Yu and Ligeng Zhu and Chengyue Wu and Yujun Lin and Zhekai Zhang and Muyang Li and Junyu Chen and Han Cai and Bingchen Liu and Daquan Zhou and Song Han},
year={2025},
eprint={2501.18427},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.18427},
}
@misc{dallabetta2024fundussimpletousenewsscraper,
title={Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions},
author={Max Dallabetta and Conrad Dobberstein and Adrian Breiding and Alan Akbik},
year={2024},
eprint={2403.15279},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2403.15279},
}
@misc{lambert2025tulu3pushingfrontiers,
title={Tulu 3: Pushing Frontiers in Open Language Model Post-Training},
author={Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi},
year={2025},
eprint={2411.15124},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.15124},
}
@misc{zheng2024lmsyschat1mlargescalerealworldllm,
title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
year={2024},
eprint={2309.11998},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.11998},
}
@misc{gosling2023pippapartiallysyntheticconversational,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.05884},
}
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Model tree for PJMixers-Dev/Granite-3.1-Earthen-v0.3-1B-A400M-GGUF
Base model
ibm-granite/granite-3.1-1b-a400m-base