---
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.