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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NPC Agentic 7B (v1)

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

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:

@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.

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for ramankrishna10/npc-agentic-7b-v3

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(2569)

this model

Quantizations

2 models