Instructions to use ramankrishna10/npc-coder-1.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramankrishna10/npc-coder-1.5b-gguf with Transformers:

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

pipe = pipeline("text-generation", model="ramankrishna10/npc-coder-1.5b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ramankrishna10/npc-coder-1.5b-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ramankrishna10/npc-coder-1.5b-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramankrishna10/npc-coder-1.5b-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-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ramankrishna10/npc-coder-1.5b-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-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ramankrishna10/npc-coder-1.5b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ramankrishna10/npc-coder-1.5b-gguf"
# 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-coder-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M

SGLang

How to use ramankrishna10/npc-coder-1.5b-gguf 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-coder-1.5b-gguf" \
    --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-coder-1.5b-gguf",
		"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-coder-1.5b-gguf" \
        --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-coder-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ramankrishna10/npc-coder-1.5b-gguf with Ollama:
```
ollama run hf.co/ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M
```

Unsloth Studio

How to use ramankrishna10/npc-coder-1.5b-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-coder-1.5b-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-coder-1.5b-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-coder-1.5b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use ramankrishna10/npc-coder-1.5b-gguf with Docker Model Runner:
```
docker model run hf.co/ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M
```

Lemonade

How to use ramankrishna10/npc-coder-1.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramankrishna10/npc-coder-1.5b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.npc-coder-1.5b-gguf-Q4_K_M

List all available models

lemonade list

npc-coder-1.5b-gguf / README.md

ramankrishna10

NPC Coder 1.5B GGUF quants (q4_k_m/q5_k_m/q8_0/f16)

cb904a0 verified about 2 months ago

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	---
	license: apache-2.0
	language: [en]
	library_name: transformers
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	tags: [code, reasoning, think, local, npc, laconic]
	pipeline_tag: text-generation
	---

	# NPC Coder 1.5B

	A local-first coding agent with visible `<think>` reasoning, a laconic
	senior-engineer voice, and an honest-failure character (it flags uncertainty
	instead of inventing APIs). Built on Qwen2.5-Coder-1.5B-Instruct. Runs on a
	laptop in GGUF.

	## What it is

	- Visible step-by-step reasoning in `<think>` blocks before answering
	- Terse, here's-the-fix answers (no filler)
	- Admits uncertainty on hard or obscure problems rather than hallucinating
	- Stable NPC identity (does not claim to be Qwen)

	## Honest capability framing

	This is a 1.5B model. It handles easy-to-medium coding and debugging
	competently and reasons visibly about them. It is NOT an olympiad-level
	solver — on genuinely hard algorithmic problems the reasoning can be
	incomplete, and the model is trained to SAY so rather than emit
	confident-but-wrong solutions. Treat it as a fast local assistant for everyday
	coding, not a replacement for a frontier model on hard problems.

	It can still be overconfident on obscure factual trivia (exact default
	arguments, precise version numbers) — the honest-failure training mitigates
	but does not eliminate this at 1.5B. Verify specifics against the docs.

	Benchmark: HumanEval (instruct, pass@1, greedy): 65.9%. Measured with
	`lm-eval-harness` `humaneval_instruct`. (The personality fine-tune slightly
	improved the extractable-code rate vs. the reasoning-only stage, because
	terser answers parse more cleanly.)

	## Personality behavior (held-out eval, 200 prompts)

	\| behavior \| result \|
	\|---\|---\|
	\| Correct NPC identity when asked \| 100% \|
	\| No identity mention on neutral coding (over-emission) \| 2.5% \|
	\| Denies being Qwen / wrong maker \| 100% \|
	\| Flags uncertainty on unknown/obscure APIs \| 100% \|

	## Training

	- Stage 1 — reasoning: SFT on `open-r1/codeforces-cots` (decontaminated
	Python subsets, fit-filtered to ≤8192 tokens so every `<think>` trace is
	complete; the filter biases toward shorter, laconic traces). 15k traces.
	- Stage 2 — voice + identity + honest-failure: SFT with a 7k-example
	personality set (gated identity, a large anti-over-emission cohort, an
	honest-failure cohort, and a 1k anti-forgetting buffer of Stage-1 reasoning
	data). LoRA, gentle LR, both stages merged.

	Apache 2.0 model. Reasoning data: `open-r1/codeforces-cots` (CC-BY-4.0 / ODC-By,
	attributed).

	## Local use

	GGUF quants: q4_k_m (~941 MB, laptop default), q5_k_m (~1.1 GB), q8_0
	(~1.6 GB), f16 (~3.1 GB). At q4_k_m, ~7 tok/s on CPU. Uses the standard ChatML
	(`<\|im_start\|>` / `<\|im_end\|>`) template.

	If q4_k_m's coherence on edge cases matters to you, q5_k_m is a cleaner default.

	## Attribution & author

	Reasoning data: `open-r1/codeforces-cots` (HuggingFace Open-R1), CC-BY-4.0.
	Base model: `Qwen/Qwen2.5-Coder-1.5B-Instruct`, Apache 2.0.
	Author: Rama Krishna Bachu / Bottensor (Independent Research). ORCID 0009-0000-1298-0681.