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SutanshuRaj
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Model Details
Model Provider
SutanshuRaj
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
GoudERP keeps Qwen3's reasoning mode: it writes its reasoning inside a <think> block before the answer.
bash
vllm serve SutanshuRaj/GoudERP --reasoning-parser qwen3 --max-model-len 40960python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SutanshuRaj/GoudERP"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto") messages = [{"role": "user", "content": "How do I set up an intercompany balancing rule between two primary ledgers?"}]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, top_k=20)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))Recommended sampling: temperature 0.6, top-p 0.95, top-k 20 (the Qwen3 reasoning-mode defaults).
Important: close the reasoning tag. In 1.7% of our benchmark answers the model ends generation without emitting
</think>, and the answer sits inside the unclosed block. A client that hides unclosed reasoning will show the user
nothing. Close the block at the end of generation instead of discarding it.
| Stage | What it does |
|---|---|
| Domain-adaptive pre-training | ERP text (documentation, API references, schemas, filtered web) mixed with general text |
| SFT Phase 1 | ERP conversations (single-turn, multiple-choice, multi-turn) mixed with general instruction data |
| SFT Phase 2 | ERP reasoning, business rules, tool calling, and general reasoning data |
| TIES merge | For each weight tensor, the 30% of entries that fine-tuning moved furthest are pulled 70% of the way back to Qwen3-8B |
The merge: theta_final = theta_SFT + 0.7 * trim_0.3(theta_base - theta_SFT).
Training data were generated from an ERP taxonomy: search queries and questions written by an LLM, web pages retrieved and scored for relevance, and answers grounded in the retained pages.
Our ERP benchmark holds single-turn, multiple-choice, and multi-turn questions across ERP modules, processes, and
complexity levels. 2,614 turns (1,832 conversations) answered by every model. Answers are scored by the open-weight judge
gpt-oss-120b, which sees them without model names and with reasoning removed. Confidence intervals and p-values
come from a paired bootstrap over conversations (4,000 resamples).
| Model | Params | Overall | Accuracy |
|---|---|---|---|
| GoudERP-8B | 8B | 87.7 | 87.1 |
| Nemotron-Cascade-2-30B-A3B | 30B (3B active) | 87.6 | 85.3 |
| Nemotron-3-Nano-30B-A3B | 30B (3B active) | 87.1 | 86.2 |
| Qwen3-235B-A22B | 235B (22B active) | 87.0 | 86.5 |
| Qwen3-32B | 32B | 86.9 | 86.5 |
| Sarvam-105B | 105B | 86.1 | 86.1 |
| Qwen3.5-397B-A17B | 397B (17B active) | 85.8 | 86.2 |
| Qwen3-8B (base) | 8B | 85.1 | 85.2 |
| Gemma-4-26B-A4B-it | 26B (4B active) | 84.3 | 85.6 |
GoudERP has the highest accuracy of every model compared (p <= 0.016) and the highest overall score,
statistically tied with Nemotron-Cascade-2-30B-A3B (p = 0.51) and ahead of the rest (p < 0.001).
| Benchmark | Qwen3-8B (base) | After fine-tuning | GoudERP-8B |
|---|---|---|---|
| GSM8K | 95.1 | 72.3 | 95.2 |
| MATH-500 | 95.8 | 63.0 | 95.0 |
| AIME 2024 | 74.8 | 52.2 | 74.9 |
| AIME 2025 | 64.8 | 32.3 | 62.8 |
| MMLU-Pro | 73.1 | 46.5 | 75.4 |
| IFEval | 84.1 | 52.1 | 79.5 |
AIME is averaged over 64 samples per problem; IFEval is prompt-level strict accuracy. All runs use the sampling settings above.
BFCL v4, restricted to its 17 standard categories, so the overall column is not comparable to the public BFCL leaderboard:
| Model | Overall | Non-live | Live | Multi-turn | Hallucination |
|---|---|---|---|---|---|
| Qwen3.5-397B-A17B | 72.5 | 85.0 | 82.1 | 60.8 | 86.0 |
| Nemotron-3-Nano-30B-A3B | 66.1 | 82.4 | 80.5 | 50.1 | 83.0 |
| Gemma-4-26B-A4B-it | 61.6 | 75.5 | 80.8 | 44.4 | 79.9 |
| Qwen3-8B (base) | 58.7 | 84.1 | 80.9 | 35.8 | 79.7 |
| GoudERP-8B | 58.5 | 83.9 | 80.3 | 35.3 | 81.2 |
| Nemotron-Cascade-2-30B-A3B | 54.2 | 40.4 | 77.3 | 46.4 | 68.2 |
On tau2-bench (pass^1, 3 trials, gpt-oss-120b as the user simulator; not leaderboard-comparable) GoudERP scores
airline 0.427, retail 0.307, telecom 0.211, against 0.273 / 0.342 / 0.170 for Qwen3-8B. Both Nemotron 30B-A3B models
are ahead of it on these tasks.
| Model | Held-out ERP text | WikiText-2 |
|---|---|---|
| Qwen3-8B (base) | 5.61 | 8.36 |
| After fine-tuning | 3.27 | 11.18 |
| GoudERP-8B | 3.86 | 8.50 |
Apache-2.0, following the Qwen3-8B base model.
vllm serve SutanshuRaj/GoudERP --reasoning-parser qwen3 --max-model-len 40960from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SutanshuRaj/GoudERP"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto") messages = [{"role": "user", "content": "How do I set up an intercompany balancing rule between two primary ledgers?"}]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, top_k=20)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))