Instructions to use ConicCat/Guten-SFT-LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ConicCat/Guten-SFT-LORA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3.1-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "ConicCat/Guten-SFT-LORA")

Transformers

How to use ConicCat/Guten-SFT-LORA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ConicCat/Guten-SFT-LORA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ConicCat/Guten-SFT-LORA")
model = AutoModelForCausalLM.from_pretrained("ConicCat/Guten-SFT-LORA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ConicCat/Guten-SFT-LORA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ConicCat/Guten-SFT-LORA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConicCat/Guten-SFT-LORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ConicCat/Guten-SFT-LORA

SGLang

How to use ConicCat/Guten-SFT-LORA 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 "ConicCat/Guten-SFT-LORA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConicCat/Guten-SFT-LORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ConicCat/Guten-SFT-LORA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConicCat/Guten-SFT-LORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ConicCat/Guten-SFT-LORA with Docker Model Runner:
```
docker model run hf.co/ConicCat/Guten-SFT-LORA
```

See axolotl config

axolotl version: 0.13.2

base_model: allenai/Olmo-3.1-32B-Instruct

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false

load_in_8bit: true
load_in_4bit: false

sequence_len: 6144
max_sample_length: 6144

sample_packing: true
gradient_checkpointing: true
flash_attention: true


bf16: true
tf32: true


datasets:
  - path: ./gutenbeg-2-sft-train.jsonl
    type: chat_template
    train_on_eos: 'all'

  - path: ./condor-sft-train.jsonl
    type: chat_template
    train_on_eos: 'all'

chat_template: chatml
eot_tokens: 
    - "<|im_end|>"

adapter: lora
lora_r: 64
lora_alpha: 128
lora_dropout: 0.0
lora_bias: None
lora_target_linear: true
use_tensorboard: true

optimizer: paged_adamw_8bit
learning_rate: 2.5e-5
loraplus_lr_ratio: 16

# Training arguments
output_dir: ./Olmo-Guten-SFT
num_epochs: 2
micro_batch_size: 2
gradient_accumulation_steps: 4
warmup_ratio: 0.05
lr_scheduler: 'constant_with_warmup'
max_grad_norm: 1
logging_steps: 1
seed: 42

Olmo-Guten-SFT

This model is a fine-tuned version of allenai/Olmo-3.1-32B-Instruct on the ./gutenbeg-2-sft-train.jsonl and the ./condor-sft-train.jsonl datasets.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2.5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 2
training_steps: 42

Training results

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.8.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

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