Instructions to use ramankrishna10/npc-agentic-7b-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramankrishna10/npc-agentic-7b-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ramankrishna10/npc-agentic-7b-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ramankrishna10/npc-agentic-7b-v3")
model = AutoModelForCausalLM.from_pretrained("ramankrishna10/npc-agentic-7b-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ramankrishna10/npc-agentic-7b-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ramankrishna10/npc-agentic-7b-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ramankrishna10/npc-agentic-7b-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ramankrishna10/npc-agentic-7b-v3

SGLang

How to use ramankrishna10/npc-agentic-7b-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ramankrishna10/npc-agentic-7b-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ramankrishna10/npc-agentic-7b-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ramankrishna10/npc-agentic-7b-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ramankrishna10/npc-agentic-7b-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ramankrishna10/npc-agentic-7b-v3 with Docker Model Runner:
```
docker model run hf.co/ramankrishna10/npc-agentic-7b-v3
```

npc-agentic-7b-v3 / README.md

ramankrishna10

docs: add Zenodo citation (10.5281/zenodo.19954103)

716e8d7 verified about 1 month ago

preview code

raw

history blame contribute delete

4.41 kB

	---
	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).