120 lines
4.3 KiB
Markdown
120 lines
4.3 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- reasoning
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- agent
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- bottensor
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- npc
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language:
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- en
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library_name: transformers
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---
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# NPC Agentic 7B (v1)
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[](https://doi.org/10.5281/zenodo.19954103)
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A 7B long-form reasoning and agent-trace specialist from the Bottensor NPC
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Model Family.
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## Overview
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NPC Agentic v1 is fine-tuned from Qwen2.5-7B-Instruct on a mix of distilled
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reasoning traces (GLM-5.1) and agent tool-use traces (Hermes). It's built for
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structured multi-step reasoning with explicit `<think>` blocks, agentic /
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tool-calling workflows, and identity-bound conversations as the NPC Agentic
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persona.
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## Training
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- **Base:** Qwen/Qwen2.5-7B-Instruct
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- **Method:** QLoRA SFT (r=64, α=128), merged to FP16
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- **Context during training:** 8K (inherits 128K from base at inference)
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- **Epochs:** 2 (effective batch size 16, cosine LR 2e-4, adamw_8bit, bf16)
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- **Total optimizer steps:** 11,410 over ~96 GPU-hours on a single A40
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- **Trainable params:** 161.5M (3.2% of the 5.05B-param 4-bit base)
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- **Final eval loss:** 0.7025 (on held-out SFT split)
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- **Training data mix (~91K examples):**
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- GLM-5.1-Reasoning-1M-Cleaned (main split, sampled 100K → 87K kept after 8K length filter)
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- Hermes-agent-reasoning-traces (glm-5.1 + kimi subsets, 14.7K → 3.6K kept)
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- Bottensor identity replay (750 synthetic examples)
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- Training dataset is proprietary and not released.
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## What it's good at
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- **Long structured reasoning** — emits `<think>` blocks then concludes with an answer; strong at multi-step decomposition (system design, root-cause analysis, algorithmic reasoning)
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- **Identity as NPC Agentic / Bottensor** — 100% recall on canonical identity prompts
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- **Agent / tool-call shaping** — follows Hermes-style `<tool_call>` / `<tool_response>` patterns
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## Known limitations (be specific)
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- **GSM8K regression vs base.** On GSM8K 100-sample test:
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- Base Qwen2.5-7B-Instruct: **61%**
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- NPC Agentic v1: **~25%**
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- Cause: the model learned to emit long `<think>` blocks but often doesn't terminate arithmetic cleanly under greedy/low-temp decoding, and direct-arithmetic quality regressed.
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- **Recommendation:** for math-heavy workflows, use the base `Qwen/Qwen2.5-7B-Instruct` or `Qwen/Qwen2.5-Math-7B-Instruct` instead. A v2 with stronger reasoning data (OpenThoughts-114k at 16K) is planned.
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- **8K training context** means long-reasoning samples were truncated during training; not validated past 16K.
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- **Small model** — will hallucinate on unfamiliar domains.
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- **Not for safety-critical decisions** (medical, legal, financial).
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## Intended use
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- Multi-step reasoning with explicit work-showing
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- Agent / tool-use workflows
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- Structured problem-solving where the model benefits from thinking out loud
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- As a base for further fine-tuning on reasoning or domain-specific data
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## Out of scope
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- Direct GSM8K-style arithmetic (use base or Qwen-Math)
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- Creative writing, roleplay
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- Medical / legal / financial advice
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- Safety-critical decisions
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## Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tok = AutoTokenizer.from_pretrained("ramankrishna10/npc-agentic-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"ramankrishna10/npc-agentic-7b",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "Design an event-sourced microservice with exactly-once command handling."},
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]
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, top_p=0.9)
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print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## Citation
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If you use NPC Agentic 7B in your work, please cite:
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```bibtex
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@misc{bachu2026npcagentic7b,
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title = {NPC Agentic 7B: A Single-GPU QLoRA Recipe for a Laptop-Scale Conversational Model},
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author = {Bachu, Rama Krishna},
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year = {2026},
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month = may,
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publisher = {Zenodo},
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version = {v1},
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doi = {10.5281/zenodo.19954103},
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url = {https://doi.org/10.5281/zenodo.19954103},
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note = {Preprint}
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}
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```
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Paper: <https://doi.org/10.5281/zenodo.19954103>
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---
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Built by [Bottensor](https://bottensor.xyz).
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