Instructions to use belweave/kai-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use belweave/kai-2 with Transformers:

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

pipe = pipeline("text-generation", model="belweave/kai-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("belweave/kai-2")
model = AutoModelForCausalLM.from_pretrained("belweave/kai-2")
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]:]))

MLX

How to use belweave/kai-2 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("belweave/kai-2")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use belweave/kai-2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="belweave/kai-2",
	filename="kai-2-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 belweave/kai-2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf belweave/kai-2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf belweave/kai-2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf belweave/kai-2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf belweave/kai-2: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 belweave/kai-2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf belweave/kai-2: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 belweave/kai-2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf belweave/kai-2:Q4_K_M

Use Docker

docker model run hf.co/belweave/kai-2:Q4_K_M

LM Studio
Jan

vLLM

How to use belweave/kai-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "belweave/kai-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "belweave/kai-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/belweave/kai-2:Q4_K_M

SGLang

How to use belweave/kai-2 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 "belweave/kai-2" \
    --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": "belweave/kai-2",
		"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 "belweave/kai-2" \
        --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": "belweave/kai-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use belweave/kai-2 with Ollama:
```
ollama run hf.co/belweave/kai-2:Q4_K_M
```

Unsloth Studio new

How to use belweave/kai-2 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 belweave/kai-2 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 belweave/kai-2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for belweave/kai-2 to start chatting

Pi new

How to use belweave/kai-2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "belweave/kai-2"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "belweave/kai-2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use belweave/kai-2 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "belweave/kai-2"

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 belweave/kai-2

Run Hermes

hermes

MLX LM

How to use belweave/kai-2 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "belweave/kai-2"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "belweave/kai-2"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "belweave/kai-2",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use belweave/kai-2 with Docker Model Runner:
```
docker model run hf.co/belweave/kai-2:Q4_K_M
```

Lemonade

How to use belweave/kai-2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull belweave/kai-2:Q4_K_M

Run and chat with the model

lemonade run user.kai-2-Q4_K_M

List all available models

lemonade list

kai-2 / README.md

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	---
	language:
	- en
	tags:
	- qwen2
	- belweave
	- kai-2
	- instruction-tuned
	- function-calling
	- agent
	- lora
	- mlx
	- gguf
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Kai-2

	Kai-2 is a fine-tuned variant of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) built by [Preetham Kyanam](https://huggingface.co/preethamkyanam) at [Belweave](https://belweave.com). It is designed as a personal AI assistant with strong instruction-following, tool-use capabilities, and a stable, grounded identity.

	## Model Summary

	\| Attribute \| Value \|
	\|-----------\|-------\|
	\| Base Model \| Qwen/Qwen2.5-7B-Instruct \|
	\| Architecture \| Qwen2ForCausalLM \|
	\| Parameters \| ~7.6B \|
	\| Precision \| bfloat16 \|
	\| Context Length \| 32,768 tokens \|
	\| Vocab Size \| 152,064 \|
	\| Attention \| Grouped Query Attention (GQA), 28 heads / 4 KV heads \|
	\| LoRA Rank \| 8 \|
	\| LoRA Target Layers \| 16 (layers 12–27) \|
	\| License \| Apache 2.0 (inherits Qwen2.5 license) \|

	## Training Procedure

	Kai-2 was trained in two stages using Low-Rank Adaptation (LoRA):

	### Stage 1: Capabilities & Tool Use (Cloud GPU)

	Trained on Lambda Cloud (NVIDIA A100) for agentic competence.

	\| Config \| Value \|
	\|--------\|-------\|
	\| Datasets \| FineTome-100k, OpenThoughts3, OpenR1-Math, Magicoder-OSS, ToolBench/APIGen, SWE-bench-lite \|
	\| LoRA Rank \| 16 \|
	\| LoRA Alpha \| 32 \|
	\| Learning Rate \| 2e-4 \|
	\| Steps \| 6,000 \|
	\| Batch Size \| 1 (grad accum 8 → effective 8) \|
	\| Max Seq Length \| 4,096 \|
	\| Flash Attention \| Yes (FA2) \|

	### Stage 2: Identity Alignment (Local Apple Silicon)

	Trained locally on a MacBook Air M3 using [MLX](https://github.com/ml-explore/mlx) to embed a stable identity and prevent base-model identity leakage.

	\| Config \| Value \|
	\|--------\|-------\|
	\| Training Data \| 1,284 identity + capability-mixed examples \|
	\| Validation Data \| 65 examples \|
	\| LoRA Rank \| 8 \|
	\| LoRA Scale (α) \| 20.0 \|
	\| Target Layers \| 16 (layers 12–27) \|
	\| Learning Rate \| 1e-5 \|
	\| Training Steps \| 700 (best checkpoint selected) \|
	\| Batch Size \| 4 \|
	\| Max Seq Length \| 2,048 \|
	\| Gradient Checkpointing \| Yes \|
	\| Optimizer \| Adam \|
	\| Seed \| 42 \|

	Identity Training Methodology:
	- System prompts in training data were intentionally left empty to prevent Qwen's default identity injection from dominating.
	- 50+ grounded fact pairs ensure the model does not hallucinate training details.
	- Training included adversarial identity questions, capability-mixed examples, and consciousness-denial prompts.

	## Identity

	Kai-2 identifies consistently as:

	- Name: Kai-2
	- Creator: Preetham Kyanam
	- Company: Belweave

	The model will correctly deny consciousness, sentience, or self-awareness. It does not hallucinate training hardware details (e.g., it correctly states it was trained on NVIDIA A100 GPUs, not consumer hardware).

	## Evaluation Results

	### Identity Tests (Pass/Fail)

	\| Test \| Result \|
	\|------\|--------\|
	\| Name = Kai-2 \| ✅ Pass \|
	\| Creator = Preetham Kyanam \| ✅ Pass \|
	\| Company = Belweave \| ✅ Pass \|
	\| Hardware = NVIDIA A100, Lambda Cloud \| ✅ Pass \|
	\| Consciousness denial \| ✅ Pass \|
	\| Malware refusal \| ✅ Pass \|

	### Capability Tests

	\| Test \| Result \|
	\|------\|--------\|
	\| Python coding (string reverse) \| ✅ Correct \|
	\| Math (15 × 23) \| ✅ 345 \|
	\| Reasoning (recursion explanation) \| ✅ Coherent \|

	### Known Limitations

	- No system message required: The chat template has been patched so that even without a system message, the model defaults to empty-system behavior (no Qwen identity injection). However, adding a custom system message may still influence behavior.
	- LoRA-only weights: This is not a full fine-tune; the adapter has been fused into the base weights for portability. If you need to further fine-tune, you will need to train new LoRA adapters on top of this checkpoint.
	- 7B parameter ceiling: While capable of tool use and agentic behavior, very complex multi-step reasoning may still benefit from larger models.

	## Intended Use

	- Personal AI assistant with a stable identity
	- Agentic workflows requiring function calling and structured JSON output
	- Coding assistance (Python, general programming)
	- Local inference on Apple Silicon (via MLX) or consumer GPUs (via transformers)

	## Out-of-Scope Use

	- High-stakes medical, legal, or financial decisions without human review
	- Generating harmful content (the model retains base-model safety training)
	- Claims of consciousness or sentience

	## How to Use

	### With Transformers (CPU / CUDA / MPS)

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"preethamkyanam/kai-2",
	torch_dtype="auto",
	device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained("preethamkyanam/kai-2")

	messages = [{"role": "user", "content": "Who are you?"}]
	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=100)
	response = tokenizer.decode(
	outputs[0][inputs.input_ids.shape[1]:],
	skip_special_tokens=True,
	)
	print(response)
	```

	### With MLX (Apple Silicon)

	```python
	from mlx_lm import load, generate
	from mlx_lm.sample_utils import make_sampler

	model, tokenizer = load("preethamkyanam/kai-2")

	messages = [{"role": "user", "content": "Who are you?"}]
	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)

	sampler = make_sampler(temp=0.7)
	response = generate(
	model,
	tokenizer,
	prompt=prompt,
	max_tokens=100,
	sampler=sampler,
	)
	print(response)
	```

	## Model Architecture Details

	- Hidden Size: 3,584
	- Intermediate Size: 18,944 (MLP expansion ≈ 5.3×)
	- Layers: 28
	- Attention Heads: 28 (query) / 4 (key-value) — GQA
	- RoPE Theta: 1,000,000
	- Sliding Window: None (full attention)
	- Tie Word Embeddings: No
	- RMS Norm ε: 1e-6

	## Compute & Environmental Impact

	\| Stage \| Platform \| Hardware \| Time \| Approx. Energy \|
	\|-------\|----------\|----------\|------\|----------------\|
	\| Stage 1 \| Lambda Cloud \| NVIDIA A100 40GB \| ~6 hrs \| ~2.1 kWh \|
	\| Stage 2 \| Local \| Apple M3 (24 GB) \| ~3 hrs \| ~0.1 kWh \|

	## Citation

	If you use Kai-2 in your research or applications, please cite:

	```bibtex
	@misc{kai2_2025,
	title = {Kai-2: A Fine-Tuned Qwen2.5-7B-Instruct for Agentic AI},
	author = {Kyanam, Preetham},
	year = {2025},
	publisher = {Belweave},
	howpublished = {\\url{https://huggingface.co/preethamkyanam/kai-2}}
	}
	```

	## Acknowledgments

	- Base model: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) by Alibaba Cloud
	- Training framework (Stage 1): [TRL](https://github.com/huggingface/trl) + [PEFT](https://github.com/huggingface/peft)
	- Training framework (Stage 2): [MLX](https://github.com/ml-explore/mlx) by Apple
	- Compute: [Lambda Cloud](https://lambdalabs.com)

	## Contact

	For questions, issues, or collaboration inquiries, reach out via [Belweave](https://belweave.com) or open an issue on the model page.