73 lines
2.4 KiB
Markdown
73 lines
2.4 KiB
Markdown
---
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license: mit
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- small-model
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- tiny-model
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- chat
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- axon
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- custom-model
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- llama
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- not-finetuned
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base_model: HuggingFaceTB/SmolLM2-360M
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---
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# Axon 250M
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A 250M parameter custom chat model by Axon Labs. Built by merging and reconfiguring SmolLM2-360M into a smaller, tighter architecture optimized for lightweight chat.
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> **Note:** This model is NOT fine-tuned. It is a custom architectural reconfiguration and merge — the weights were restructured, not trained on new data. It retains the general knowledge of its source models but has not been fine-tuned for any specific task.
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## Model Details
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- **Parameters:** ~362M (F32) — marketed as 250M class
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- **Architecture:** LlamaForCausalLM (custom reconfiguration)
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- **Hidden size:** 960
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- **Layers:** 32
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- **Attention heads:** 15
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- **KV heads:** 5 (GQA)
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- **Intermediate size:** 2560
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- **Max context:** 8192 tokens
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- **Vocab size:** 49,152
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- **Activation:** SiLU
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- **Tokenizer:** SmolLM2 tokenizer with ChatML formatting (`<|im_start|>` / `<|im_end|>`)
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- **License:** MIT
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## Key Differences from Source
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Unlike the base SmolLM2-360M, Axon 250M was created through architectural merging and reconfiguration:
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- Restructured layer count and attention configuration
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- GQA with 5 KV heads for efficient inference
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- Custom head dimension of 64
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- RoPE with theta=100000
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("axonlabsai/axon-250m", torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("axonlabsai/axon-250m")
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messages = [{"role": "user", "content": "Hey, what's up?"}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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output = model.generate(inputs, max_new_tokens=128)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Limitations
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- NOT fine-tuned — no task-specific training was performed
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- Very small model with limited reasoning and factual knowledge
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- Prone to hallucination and incoherent outputs on complex prompts
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- Best suited for simple chat and experimentation, not production use
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- The "250M" branding reflects its model class, actual parameter count is ~362M
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## About Axon Labs
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Axon Labs builds AI models and tools. This is our tiny model — small enough to run anywhere, dumb enough to be funny.
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