Instructions to use strykes/emberforge-3b-reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use strykes/emberforge-3b-reasoner with Transformers:

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

pipe = pipeline("text-generation", model="strykes/emberforge-3b-reasoner")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("strykes/emberforge-3b-reasoner", dtype="auto")

PEFT
How to use strykes/emberforge-3b-reasoner with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use strykes/emberforge-3b-reasoner with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="strykes/emberforge-3b-reasoner",
	filename="gguf/Nanbeige4.1-3B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use strykes/emberforge-3b-reasoner with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf strykes/emberforge-3b-reasoner:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf strykes/emberforge-3b-reasoner:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf strykes/emberforge-3b-reasoner:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf strykes/emberforge-3b-reasoner: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 strykes/emberforge-3b-reasoner:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf strykes/emberforge-3b-reasoner: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 strykes/emberforge-3b-reasoner:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf strykes/emberforge-3b-reasoner:Q4_K_M

Use Docker

docker model run hf.co/strykes/emberforge-3b-reasoner:Q4_K_M

LM Studio
Jan

vLLM

How to use strykes/emberforge-3b-reasoner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "strykes/emberforge-3b-reasoner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "strykes/emberforge-3b-reasoner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/strykes/emberforge-3b-reasoner:Q4_K_M

SGLang

How to use strykes/emberforge-3b-reasoner 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 "strykes/emberforge-3b-reasoner" \
    --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": "strykes/emberforge-3b-reasoner",
		"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 "strykes/emberforge-3b-reasoner" \
        --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": "strykes/emberforge-3b-reasoner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use strykes/emberforge-3b-reasoner with Ollama:
```
ollama run hf.co/strykes/emberforge-3b-reasoner:Q4_K_M
```

Unsloth Studio new

How to use strykes/emberforge-3b-reasoner 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 strykes/emberforge-3b-reasoner 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 strykes/emberforge-3b-reasoner to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for strykes/emberforge-3b-reasoner to start chatting

Pi new

How to use strykes/emberforge-3b-reasoner with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf strykes/emberforge-3b-reasoner:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "strykes/emberforge-3b-reasoner:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use strykes/emberforge-3b-reasoner with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf strykes/emberforge-3b-reasoner:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default strykes/emberforge-3b-reasoner:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use strykes/emberforge-3b-reasoner with Docker Model Runner:
```
docker model run hf.co/strykes/emberforge-3b-reasoner:Q4_K_M
```

Lemonade

How to use strykes/emberforge-3b-reasoner with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull strykes/emberforge-3b-reasoner:Q4_K_M

Run and chat with the model

lemonade run user.emberforge-3b-reasoner-Q4_K_M

List all available models

lemonade list

EmberForge-3B-Reasoner

Private finetuned Nanbeige4.1-3B reasoning release by strykes.

Included Artifacts

Merged full model (Safetensors) at repo root for HF benchmarking
LoRA adapter in adapter/
GGUF in gguf/:
- Nanbeige4.1-3B-Q5_K_M.gguf
- Nanbeige4.1-3B-Q4_K_M.gguf
- Nanbeige4.1-3B-f16.gguf
Optional archive in archives/

Training Snapshot

Base: Nanbeige/Nanbeige4.1-3B
Method: Unsloth QLoRA -> merged weights
Data: ~3.5k synthetic reasoning samples
Epochs: 2
Sequence length: 4096

Notes

Intended for research and benchmarking.
Validate outputs before critical use.

Benchmarks (2026-02-24)

Local lm-eval results (this finetune)

Task	Metric	Score
mmlu	acc,none	59.98%
gsm8k	exact_match,flexible-extract	62.40%
arc_challenge	acc_norm,none	31.74%
hellaswag	acc_norm,none	56.07%
winogrande	acc,none	50.04%
piqa	acc_norm,none	63.22%
boolq	acc,none	74.37%
truthfulqa_mc2	acc,none	45.34%

Public references

Base model (Nanbeige/Nanbeige4.1-3B) author-published benchmarks are listed in:
- benchmarks/lm-eval-2026-02-24/benchmark_comparison_public_2026-02-24.md
Frontier references (Claude/GPT/Gemini) are included in the same comparison report.

Reproducibility artifacts

benchmarks/lm-eval-2026-02-24/summary_v3.tsv
benchmarks/lm-eval-2026-02-24/results_2026-02-24T00-06-21.474293.json
benchmarks/lm-eval-2026-02-24/run_v3.log
benchmarks/lm-eval-2026-02-24/benchmark_comparison_public_2026-02-24.md

Caveat

Public model-card comparisons are not always apples-to-apples with lm-evaluation-harness settings (prompting, few-shot, decoding, and benchmark versions can differ).

Downloads last month: 44

Safetensors

Model size

4B params

Tensor type

F16

Model tree for strykes/emberforge-3b-reasoner

Base model

Nanbeige/Nanbeige4-3B-Base

Finetuned

Nanbeige/Nanbeige4.1-3B

Quantized

(56)

this model

Quantizations

1 model