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Model: belweave/kai-2 Source: Original Platform
2026-09-11 22:58:18 +08:00
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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.