初始化项目，由ModelHub XC社区提供模型

Model: AlienKevin/marin-8b-instruct-sft-terminalcorpus
Source: Original Platform

README.md

@@ -0,0 +1,73 @@
+---
+license: apache-2.0
+base_model: marin-community/marin-8b-instruct
+datasets:
+  - nvidia/Nemotron-Terminal-Corpus
+tags:
+  - terminal
+  - agentic
+  - sft
+  - llama
+  - marin
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Marin-8B-Instruct SFT on TerminalCorpus
+
+Marin-8B Instruct fine-tuned on [nvidia/Nemotron-Terminal-Corpus](https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus) (366K terminal agent trajectories).
+
+## Model Details
+
+| Parameter | Value |
+|---|---|
+| Base model | [marin-community/marin-8b-instruct](https://huggingface.co/marin-community/marin-8b-instruct) |
+| Architecture | Llama 3 8B (32 layers, 4096 hidden, 32 heads, 8 KV heads) |
+| Tokenizer | [marin-community/marin-tokenizer](https://huggingface.co/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](https://arxiv.org/abs/2602.21193) 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](https://github.com/marin-community/marin/issues/4420)
+
+## Usage
+
+```python
+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