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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ramankrishna10/npc-coder-1.5b")
model = AutoModelForCausalLM.from_pretrained("ramankrishna10/npc-coder-1.5b")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

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.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for ramankrishna10/npc-coder-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Finetuned

(164)

this model