marin-8b-instruct-sft-terminalcorpus/README.md

license, base_model, datasets, tags, language, pipeline_tag

license	base_model	datasets	tags	language	pipeline_tag
apache-2.0	marin-community/marin-8b-instruct	nvidia/Nemotron-Terminal-Corpus	terminal agentic sft llama marin	en	text-generation

Marin-8B-Instruct SFT on TerminalCorpus

Marin-8B Instruct fine-tuned on nvidia/Nemotron-Terminal-Corpus (366K terminal agent trajectories).

Model Details

Parameter	Value
Base model	marin-community/marin-8b-instruct
Architecture	Llama 3 8B (32 layers, 4096 hidden, 32 heads, 8 KV heads)
Tokenizer	marin-community/marin-tokenizer
Training data	nvidia/Nemotron-Terminal-Corpus (366K examples, all 4 subsets)
Epochs	2
Training steps	5,721
Batch size	128
Sequence length	32,768
Learning rate	2e-5 (cosine, 10% warmup)
Optimizer	AdamW (β=0.9/0.95), grad_clip=1.0, wd=1e-4
TPU	v5p-64
Final loss	0.442

Evaluation Results

Terminal-Bench 2.0

Model	TB2 Accuracy
Marin-8B Instruct (no SFT)	0/89 = 0%
Marin-8B Instruct + TerminalCorpus SFT	1/89 = 1.1%
NemotronTerminal-8B (Qwen3-8B, paper)	13.0% ± 2.2
Marin Qwen3-8B SFT reproduction (exp3490b)	14/88 = 15.9%

TBLite Progression

Checkpoint	TBLite
Step 1500 (26%)	1/100 = 1%
Step 3000 (52%)	5/100 = 5%

Training Details

Trained following the NemotronTerminal-8B paper hyperparameters. The model reaches a higher final loss (0.442 vs 0.360) as the Qwen3-8B reproduction but scores significantly lower on terminal benchmarks, likely due to architecture and tokenizer differences between Llama 3 and Qwen3.

Tracked in: marin-community/marin#4420

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AlienKevin/marin-8b-instruct-sft-terminalcorpus")
tokenizer = AutoTokenizer.from_pretrained("AlienKevin/marin-8b-instruct-sft-terminalcorpus")

License

Apache 2.0