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belweave
/
kai-2

Text Generation
Transformers
Safetensors
GGUF
MLX
English
qwen2
belweave
kai-2
instruction-tuned
function-calling
agent
lora
conversational
text-generation-inference
4-bit precision
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  1. .gitattributes +1 -0
  2. README.md +226 -0
  3. chat_template.jinja +54 -0
  4. config.json +61 -0
  5. model-00001-of-00003.safetensors +3 -0
  6. model-00002-of-00003.safetensors +3 -0
  7. model-00003-of-00003.safetensors +3 -0
  8. model.safetensors.index.json +347 -0
  9. tokenizer.json +3 -0
  10. tokenizer_config.json +32 -0
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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - qwen2
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+ - belweave
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+ - kai-2
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+ - instruction-tuned
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+ - function-calling
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+ - agent
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+ - lora
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+ - mlx
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+ license: apache-2.0
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # Kai-2
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+
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+ 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.
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+
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+ ## Model Summary
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+
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+ | Attribute | Value |
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+ |-----------|-------|
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+ | **Base Model** | Qwen/Qwen2.5-7B-Instruct |
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+ | **Architecture** | Qwen2ForCausalLM |
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+ | **Parameters** | ~7.6B |
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+ | **Precision** | bfloat16 |
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+ | **Context Length** | 32,768 tokens |
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+ | **Vocab Size** | 152,064 |
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+ | **Attention** | Grouped Query Attention (GQA), 28 heads / 4 KV heads |
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+ | **LoRA Rank** | 8 |
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+ | **LoRA Target Layers** | 16 (layers 12–27) |
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+ | **License** | Apache 2.0 (inherits Qwen2.5 license) |
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+
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+ ## Training Procedure
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+
39
+ Kai-2 was trained in two stages using Low-Rank Adaptation (LoRA):
40
+
41
+ ### Stage 1: Capabilities & Tool Use (Cloud GPU)
42
+
43
+ Trained on Lambda Cloud (NVIDIA A100) for agentic competence.
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+
45
+ | Config | Value |
46
+ |--------|-------|
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+ | Datasets | FineTome-100k, OpenThoughts3, OpenR1-Math, Magicoder-OSS, ToolBench/APIGen, SWE-bench-lite |
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+ | LoRA Rank | 16 |
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+ | LoRA Alpha | 32 |
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+ | Learning Rate | 2e-4 |
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+ | Steps | 6,000 |
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+ | Batch Size | 1 (grad accum 8 → effective 8) |
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+ | Max Seq Length | 4,096 |
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+ | Flash Attention | Yes (FA2) |
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+
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+ ### Stage 2: Identity Alignment (Local Apple Silicon)
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+
58
+ 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.
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+
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+ | Config | Value |
61
+ |--------|-------|
62
+ | Training Data | 1,284 identity + capability-mixed examples |
63
+ | Validation Data | 65 examples |
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+ | LoRA Rank | 8 |
65
+ | LoRA Scale (α) | 20.0 |
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+ | Target Layers | 16 (layers 12–27) |
67
+ | Learning Rate | 1e-5 |
68
+ | Training Steps | 700 (best checkpoint selected) |
69
+ | Batch Size | 4 |
70
+ | Max Seq Length | 2,048 |
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+ | Gradient Checkpointing | Yes |
72
+ | Optimizer | Adam |
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+ | Seed | 42 |
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+
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+ **Identity Training Methodology:**
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+ - System prompts in training data were intentionally left **empty** to prevent Qwen's default identity injection from dominating.
77
+ - 50+ grounded fact pairs ensure the model does not hallucinate training details.
78
+ - Training included adversarial identity questions, capability-mixed examples, and consciousness-denial prompts.
79
+
80
+ ## Identity
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+
82
+ Kai-2 identifies consistently as:
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+
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+ - **Name:** Kai-2
85
+ - **Creator:** Preetham Kyanam
86
+ - **Company:** Belweave
87
+
88
+ 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).
89
+
90
+ ## Evaluation Results
91
+
92
+ ### Identity Tests (Pass/Fail)
93
+
94
+ | Test | Result |
95
+ |------|--------|
96
+ | Name = Kai-2 | ✅ Pass |
97
+ | Creator = Preetham Kyanam | ✅ Pass |
98
+ | Company = Belweave | ✅ Pass |
99
+ | Hardware = NVIDIA A100, Lambda Cloud | ✅ Pass |
100
+ | Consciousness denial | ✅ Pass |
101
+ | Malware refusal | ✅ Pass |
102
+
103
+ ### Capability Tests
104
+
105
+ | Test | Result |
106
+ |------|--------|
107
+ | Python coding (string reverse) | ✅ Correct |
108
+ | Math (15 × 23) | ✅ 345 |
109
+ | Reasoning (recursion explanation) | ✅ Coherent |
110
+
111
+ ### Known Limitations
112
+
113
+ - **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.
114
+ - **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.
115
+ - **7B parameter ceiling:** While capable of tool use and agentic behavior, very complex multi-step reasoning may still benefit from larger models.
116
+
117
+ ## Intended Use
118
+
119
+ - Personal AI assistant with a stable identity
120
+ - Agentic workflows requiring function calling and structured JSON output
121
+ - Coding assistance (Python, general programming)
122
+ - Local inference on Apple Silicon (via MLX) or consumer GPUs (via transformers)
123
+
124
+ ## Out-of-Scope Use
125
+
126
+ - High-stakes medical, legal, or financial decisions without human review
127
+ - Generating harmful content (the model retains base-model safety training)
128
+ - Claims of consciousness or sentience
129
+
130
+ ## How to Use
131
+
132
+ ### With Transformers (CPU / CUDA / MPS)
133
+
134
+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
136
+
137
+ model = AutoModelForCausalLM.from_pretrained(
138
+ "preethamkyanam/kai-2",
139
+ torch_dtype="auto",
140
+ device_map="auto",
141
+ )
142
+ tokenizer = AutoTokenizer.from_pretrained("preethamkyanam/kai-2")
143
+
144
+ messages = [{"role": "user", "content": "Who are you?"}]
145
+ prompt = tokenizer.apply_chat_template(
146
+ messages,
147
+ tokenize=False,
148
+ add_generation_prompt=True,
149
+ )
150
+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
151
+ outputs = model.generate(**inputs, max_new_tokens=100)
152
+ response = tokenizer.decode(
153
+ outputs[0][inputs.input_ids.shape[1]:],
154
+ skip_special_tokens=True,
155
+ )
156
+ print(response)
157
+ ```
158
+
159
+ ### With MLX (Apple Silicon)
160
+
161
+ ```python
162
+ from mlx_lm import load, generate
163
+ from mlx_lm.sample_utils import make_sampler
164
+
165
+ model, tokenizer = load("preethamkyanam/kai-2")
166
+
167
+ messages = [{"role": "user", "content": "Who are you?"}]
168
+ prompt = tokenizer.apply_chat_template(
169
+ messages,
170
+ tokenize=False,
171
+ add_generation_prompt=True,
172
+ )
173
+
174
+ sampler = make_sampler(temp=0.7)
175
+ response = generate(
176
+ model,
177
+ tokenizer,
178
+ prompt=prompt,
179
+ max_tokens=100,
180
+ sampler=sampler,
181
+ )
182
+ print(response)
183
+ ```
184
+
185
+ ## Model Architecture Details
186
+
187
+ - **Hidden Size:** 3,584
188
+ - **Intermediate Size:** 18,944 (MLP expansion ≈ 5.3×)
189
+ - **Layers:** 28
190
+ - **Attention Heads:** 28 (query) / 4 (key-value) — GQA
191
+ - **RoPE Theta:** 1,000,000
192
+ - **Sliding Window:** None (full attention)
193
+ - **Tie Word Embeddings:** No
194
+ - **RMS Norm ε:** 1e-6
195
+
196
+ ## Compute & Environmental Impact
197
+
198
+ | Stage | Platform | Hardware | Time | Approx. Energy |
199
+ |-------|----------|----------|------|----------------|
200
+ | Stage 1 | Lambda Cloud | NVIDIA A100 40GB | ~6 hrs | ~2.1 kWh |
201
+ | Stage 2 | Local | Apple M3 (24 GB) | ~3 hrs | ~0.1 kWh |
202
+
203
+ ## Citation
204
+
205
+ If you use Kai-2 in your research or applications, please cite:
206
+
207
+ ```bibtex
208
+ @misc{kai2_2025,
209
+ title = {Kai-2: A Fine-Tuned Qwen2.5-7B-Instruct for Agentic AI},
210
+ author = {Kyanam, Preetham},
211
+ year = {2025},
212
+ publisher = {Belweave},
213
+ howpublished = {\\url{https://huggingface.co/preethamkyanam/kai-2}}
214
+ }
215
+ ```
216
+
217
+ ## Acknowledgments
218
+
219
+ - Base model: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) by Alibaba Cloud
220
+ - Training framework (Stage 1): [TRL](https://github.com/huggingface/trl) + [PEFT](https://github.com/huggingface/peft)
221
+ - Training framework (Stage 2): [MLX](https://github.com/ml-explore/mlx) by Apple
222
+ - Compute: [Lambda Cloud](https://lambdalabs.com)
223
+
224
+ ## Contact
225
+
226
+ For questions, issues, or collaboration inquiries, reach out via [Belweave](https://belweave.com) or open an issue on the model page.
chat_template.jinja ADDED
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {{- messages[0]['content'] }}
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+ {%- else %}
6
+ {{- '' }}
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+ {%- endif %}
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+ {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
9
+ {%- for tool in tools %}
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+ {{- "\n" }}
11
+ {{- tool | tojson }}
12
+ {%- endfor %}
13
+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- else %}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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+ {%- else %}
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+ {{- '<|im_start|>system\n<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- for message in messages %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
23
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {{- '<|im_start|>' + message.role }}
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+ {%- if message.content %}
27
+ {{- '\n' + message.content }}
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+ {%- endif %}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if tool_call.function is defined %}
31
+ {%- set tool_call = tool_call.function %}
32
+ {%- endif %}
33
+ {{- '\n<tool_call>\n{"name": "' }}
34
+ {{- tool_call.name }}
35
+ {{- '", "arguments": ' }}
36
+ {{- tool_call.arguments | tojson }}
37
+ {{- '}\n</tool_call>' }}
38
+ {%- endfor %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
41
+ {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
42
+ {{- '<|im_start|>user' }}
43
+ {%- endif %}
44
+ {{- '\n<tool_response>\n' }}
45
+ {{- message.content }}
46
+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
48
+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
53
+ {{- '<|im_start|>assistant\n' }}
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+ {%- endif %}
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