Instructions to use RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only with Transformers:

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

pipe = pipeline("text-generation", model="RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only")
model = AutoModelForCausalLM.from_pretrained("RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only

SGLang

How to use RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only 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 "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only" \
    --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": "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only",
		"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 "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only" \
        --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": "RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only with Docker Model Runner:
```
docker model run hf.co/RuHae/KletterMix-Ablations-OLMo3-7B-ClimbMix-Only
```

KletterMix-Ablations-OLMo3-7B-ClimbMix-Only / README.md

RuHae

Document artifact provenance and training configuration

7a4d003 verified 9 days ago

preview code

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metadata

library_name: transformers
pipeline_tag: text-generation
base_model: allenai/Olmo-3-1025-7B
tags:
  - klettermix
  - ablation
  - causal-language-modeling

OLMo 3 7B — ClimbMix only

Annealing-only run from the pretrained Stage 1 weights. The HF optimizer is not imported; a fresh optimizer and scheduler are used.

Base model

allenai/Olmo-3-1025-7B at revision stage1-step1413814. These checkpoints are base models, not instruction-tuned or chat-tuned models.

Training data

annealing data: ClimbMix only — Exactly 12B tokens: 100% ClimbMix.

Training configuration

The repository contains checkpoint revisions named step-XXXXXXX; main is the final checkpoint. Optimizer states are not part of these HF exports. The exact source paths, revision mapping, and publication code are maintained in the private KletterMix_Ablations repository.