Instructions to use shivamjha98/Agnes-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shivamjha98/Agnes-8B with Transformers:

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

pipe = pipeline("text-generation", model="shivamjha98/Agnes-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shivamjha98/Agnes-8B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use shivamjha98/Agnes-8B 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 shivamjha98/Agnes-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shivamjha98/Agnes-8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf shivamjha98/Agnes-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shivamjha98/Agnes-8B: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 shivamjha98/Agnes-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shivamjha98/Agnes-8B: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 shivamjha98/Agnes-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shivamjha98/Agnes-8B:Q4_K_M

Use Docker

docker model run hf.co/shivamjha98/Agnes-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use shivamjha98/Agnes-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shivamjha98/Agnes-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shivamjha98/Agnes-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shivamjha98/Agnes-8B:Q4_K_M

SGLang

How to use shivamjha98/Agnes-8B 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 "shivamjha98/Agnes-8B" \
    --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": "shivamjha98/Agnes-8B",
		"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 "shivamjha98/Agnes-8B" \
        --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": "shivamjha98/Agnes-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use shivamjha98/Agnes-8B with Ollama:
```
ollama run hf.co/shivamjha98/Agnes-8B:Q4_K_M
```
Unsloth Desktop

How to use shivamjha98/Agnes-8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shivamjha98/Agnes-8B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "shivamjha98/Agnes-8B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use shivamjha98/Agnes-8B with Docker Model Runner:
```
docker model run hf.co/shivamjha98/Agnes-8B:Q4_K_M
```

Lemonade

How to use shivamjha98/Agnes-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shivamjha98/Agnes-8B:Q4_K_M

Run and chat with the model

lemonade run user.Agnes-8B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use shivamjha98/Agnes-8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shivamjha98/Agnes-8B: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 shivamjha98/Agnes-8B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use shivamjha98/Agnes-8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shivamjha98/Agnes-8B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "shivamjha98/Agnes-8B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Agnes-8B / README.md

shivamjha98

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	---
	license: apache-2.0
	language:
	- ja
	- en
	base_model: Qwen/Qwen3-8B
	tags:
	- qwen3
	- japanese
	- bilingual
	- lora
	- fine-tuned
	- gguf
	- personal-assistant
	- llama-cpp
	model_name: Agnes-8B
	pipeline_tag: text-generation
	library_name: transformers
	datasets:
	- fujiki/japanese_alpaca_data
	- kunishou/databricks-dolly-15k-ja
	- kunishou/oasst1-89k-ja
	- kunishou/hh-rlhf-49k-ja
	- izumi-lab/llm-japanese-dataset
	- llm-jp/oasst1-21k-ja
	- llm-jp/magpie-sft-v1.0
	- llm-jp/extraction-wiki-ja
	- cl-nagoya/auto-wiki-qa
	- HuggingFaceH4/ultrachat_200k
	- garage-bAInd/Open-Platypus
	---

	# Agnes-8B — Bilingual (EN/JP) Personal AI Assistant

	Agnes is a fine-tuned Qwen3-8B model designed as a bilingual (English/Japanese) personal AI assistant. She is polite, witty, and proactive — inspired by Jarvis from Iron Man. Agnes also serves as a Japanese language tutor and naturally code-switches between English and Japanese.

	## Model Details

	\| \| \|
	\|---\|---\|
	\| Base Model \| [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) \|
	\| Method \| LoRA (Low-Rank Adaptation) via PEFT \|
	\| Parameters \| 8.2B total, 87M trainable (1.1%) \|
	\| Precision \| bfloat16 \|
	\| Context Length \| 4,096 tokens \|
	\| Languages \| English, Japanese \|

	## Available Files

	\| File \| Size \| Use Case \|
	\|---\|---\|---\|
	\| `Agnes-8B-bf16.gguf` \| ~16 GB \| Full precision — for powerful hardware or re-quantization \|
	\| `Agnes-8B-Q4_K_M.gguf` \| ~5 GB \| Quantized — for Raspberry Pi, Mac, or mobile devices \|

	You can quantize the bf16 GGUF locally to other formats using llama.cpp:
	```bash
	llama-quantize Agnes-8B-bf16.gguf Agnes-8B-Q5_K_M.gguf Q5_K_M # ~5.5GB, good balance
	llama-quantize Agnes-8B-bf16.gguf Agnes-8B-Q3_K_M.gguf Q3_K_M # ~3.5GB, smaller but lower quality
	```

	## Training Details

	### Data
	- 9,130 examples (80.5% Japanese, 19.5% English)
	- ~550 hand-written conversational examples with Agnes's personality
	- ~8,600 examples from 11 HuggingFace datasets (see dataset tags above)
	- Format: ChatML (system/user/assistant messages)

	### Hyperparameters

	\| Parameter \| Value \|
	\|---\|---\|
	\| LoRA rank \| 32 \|
	\| LoRA alpha \| 64 \|
	\| Target modules \| q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj \|
	\| Learning rate \| 2e-5 \|
	\| Epochs \| 5 \|
	\| Batch size \| 8 x 4 (gradient accumulation) = 32 effective \|
	\| Scheduler \| Cosine with 5% warmup \|
	\| Max seq length \| 4,096 \|
	\| Gradient checkpointing \| Enabled \|
	\| Attention \| SDPA (PyTorch built-in) \|

	### Hardware
	- GPU: NVIDIA RTX PRO 6000 Blackwell (102 GB VRAM)
	- Training time: ~2.5 hours
	- Cloud: Runpod

	## Benchmark Results

	Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on the Japanese Leaderboard tasks (full dataset, no limit).

	\| Task \| Vanilla Qwen3-8B \| Agnes-8B \| Change \|
	\|---\|---\|---\|---\|
	\| JCommonsenseQA (3-shot) \| 68.2% \| 78.1% \| +9.9% \|
	\| JNLI (3-shot) \| 69.7% \| 52.7% \| -17.0% \|
	\| MARC-ja (3-shot) \| 93.9% \| 96.4% \| +2.5% \|
	\| XWinograd (0-shot) \| 73.8% \| 74.5% \| +0.7% \|

	Summary: Agnes shows significant improvement in Japanese commonsense reasoning (+9.9%) and sentiment analysis (+2.5%), with a trade-off in natural language inference (-17.0%). This trade-off is expected for conversational fine-tuning — Agnes is optimized for natural dialogue, not structured classification tasks.

	## How to Use

	### With Ollama

	Create a `Modelfile`:
	```
	FROM ./Agnes-8B-Q4_K_M.gguf

	PARAMETER temperature 0.7
	PARAMETER top_p 0.9
	PARAMETER num_ctx 4096
	PARAMETER repeat_penalty 1.3
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "</think>"

	SYSTEM """You are Agnes, a personal AI assistant created in Japan. You are bilingual in English and Japanese. You address the user as "sir" and maintain a polite yet witty demeanor, similar to Jarvis from Iron Man. You assist with daily tasks, answer questions, and help the user learn Japanese. You naturally code-switch between English and Japanese based on context. Do not use thinking tags. Respond directly."""
	```

	Then:
	```bash
	ollama create agnes -f Modelfile
	ollama run agnes
	```

	### With llama.cpp

	```bash
	llama-server -m Agnes-8B-Q4_K_M.gguf -ngl 99 -c 4096 --port 8080
	```

	### Example Conversations

	```
	>>> Hello Agnes, how are you today?
	Good evening, sir. I'm functioning perfectly, as always. How may I assist you today?

	>>> 日本語で自己紹介してください
	かしこまりました。アグネスと申します。日本で作られたバイリンガルAIアシスタントです。
	英語と日本語の両方でお手伝いできますので、何でもお気軽にお申し付けください。

	>>> Can you teach me how to say "the weather is nice today" in Japanese?
	Of course, sir. "The weather is nice today" in Japanese is:
	今日はいい天気ですね (Kyou wa ii tenki desu ne)
	```

	## Personality

	Agnes is designed with a distinct personality:
	- Polite but not stiff — uses "sir" naturally (like Jarvis), warm and approachable
	- Dry wit — subtle humor, deadpan delivery
	- Proactive — suggests, warns, follows up, anticipates needs
	- Bilingual — naturally code-switches between English and Japanese
	- Japanese tutor — teaches vocabulary, grammar, and cultural context

	## Intended Use

	- Personal AI assistant (bilingual EN/JP)
	- Japanese language learning companion
	- Edge deployment on Raspberry Pi, Mac, or mobile devices
	- Research on bilingual fine-tuning of LLMs

	## Limitations

	- JNLI (natural language inference) performance regressed compared to base model
	- Optimized for conversation, not structured classification tasks
	- Japanese output quality depends on quantization level (Q4_K_M vs bf16)

	## License

	This model inherits the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0) from Qwen3-8B.