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Model: ramankrishna10/npc-agentic-7b-v3
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
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README.md

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

added_tokens.json

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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.6"
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