Instructions to use ramankrishna10/npc-fast-1.7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramankrishna10/npc-fast-1.7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ramankrishna10/npc-fast-1.7b-gguf",
	filename="npc-fast-1.7b-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ramankrishna10/npc-fast-1.7b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use ramankrishna10/npc-fast-1.7b-gguf with Ollama:
```
ollama run hf.co/ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
```

Unsloth Studio new

How to use ramankrishna10/npc-fast-1.7b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ramankrishna10/npc-fast-1.7b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ramankrishna10/npc-fast-1.7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ramankrishna10/npc-fast-1.7b-gguf to start chatting

Docker Model Runner
How to use ramankrishna10/npc-fast-1.7b-gguf with Docker Model Runner:
```
docker model run hf.co/ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M
```

Lemonade

How to use ramankrishna10/npc-fast-1.7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramankrishna10/npc-fast-1.7b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.npc-fast-1.7b-gguf-Q4_K_M

List all available models

lemonade list

NPC Fast 1.7B — GGUF

GGUF conversions of ramankrishna10/npc-fast-1.7b for llama.cpp / Ollama / LM Studio / local inference.

File	Size	Precision	Best for
`npc-fast-1.7b-q4_k_m.gguf`	1.1 GB	Q4_K_M	laptops, CPU inference, fastest
`npc-fast-1.7b-q8_0.gguf`	1.8 GB	Q8_0	best quality at quantized size
`npc-fast-1.7b-f16.gguf`	3.4 GB	F16	lossless, reference

See the main repo for training recipe, evaluation results, and honest limitation disclosures (short version: trained to 16K context, 128K YaRN scaling is unvalidated).

Usage with llama.cpp

./build/bin/llama-cli \
  -m npc-fast-1.7b-q4_k_m.gguf \
  -sys "You are NPC Fast. Output exactly one JSON object with fields route and reason." \
  -p "What is 2+2?" \
  -n 60 --temp 0.0

Usage with Ollama

# Create a Modelfile:
cat > Modelfile << "MOD"
FROM ./npc-fast-1.7b-q4_k_m.gguf
PARAMETER temperature 0.0
PARAMETER num_predict 60
SYSTEM "You are NPC Fast, a routing model. Output exactly one JSON object with fields route and reason."
MOD

ollama create npc-fast -f Modelfile
ollama run npc-fast "Build a DCF for TSLA."

Intended use

See the main repo. Short version: decide between self (handle locally) and npc_fin (escalate to a 32B finance specialist) for every user request, then emit {"route": "self"|"npc_fin", "reason": "<short>"}.

Limitations

Trained to 16K context. 128K YaRN scaling is configured but not validated.
Router fine-tuned on 500 synthetic pairs. OOD eval at 98.3% (n=60).
No RLHF. Refusal behavior inherited from SmolLM2-Instruct.

Credits

Built by Bottensor (a Falcon Hash company), creator: dude.npc. Base: HuggingFaceTB/SmolLM2-1.7B-Instruct.

Citation

If you use this model or build on its training recipe, please cite the accompanying preprint:

Bachu, R. K. (2026). NPC Fast 1.7B: Building a Usable Small Model on a Single H100. Zenodo. https://doi.org/10.5281/zenodo.19771040

@misc{bachu2026npcfast,
  title     = {NPC Fast 1.7B: Building a Usable Small Model on a Single H100},
  author    = {Bachu, Rama Krishna},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19771040},
  url       = {https://doi.org/10.5281/zenodo.19771040},
  note      = {Preprint},
}

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GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

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Model tree for ramankrishna10/npc-fast-1.7b-gguf

Base model

HuggingFaceTB/SmolLM2-1.7B

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HuggingFaceTB/SmolLM2-1.7B-Instruct

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