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1.72 GB
9 files
Updated 22 days ago
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| .gitattributes | 1.58 kB xet | 80ee8fc6 | |
| README.md | 2.44 kB xet | 2b011350 | |
| axon-250m-Q4_K_M.gguf | 271 MB xet | b2842cf7 | |
| chat_template.jinja | 368 Bytes xet | 14be8a51 | |
| config.json | 890 Bytes xet | 99575c57 | |
| generation_config.json | 141 Bytes xet | 28934be7 | |
| model.safetensors | 1.45 GB xet | 456a009e | |
| tokenizer.json | 3.52 MB xet | bcb0fbc1 | |
| tokenizer_config.json | 405 Bytes xet | 0f258d60 |
Axon 250M
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.
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.
Model Details
- Parameters: ~362M (F32) — marketed as 250M class
- Architecture: LlamaForCausalLM (custom reconfiguration)
- Hidden size: 960
- Layers: 32
- Attention heads: 15
- KV heads: 5 (GQA)
- Intermediate size: 2560
- Max context: 8192 tokens
- Vocab size: 49,152
- Activation: SiLU
- Tokenizer: SmolLM2 tokenizer with ChatML formatting (
<|im_start|>/<|im_end|>) - License: MIT
Key Differences from Source
Unlike the base SmolLM2-360M, Axon 250M was created through architectural merging and reconfiguration:
- Restructured layer count and attention configuration
- GQA with 5 KV heads for efficient inference
- Custom head dimension of 64
- RoPE with theta=100000
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("axonlabsai/axon-250m", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("axonlabsai/axon-250m")
messages = [{"role": "user", "content": "Hey, what's up?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=128)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Limitations
- NOT fine-tuned — no task-specific training was performed
- Very small model with limited reasoning and factual knowledge
- Prone to hallucination and incoherent outputs on complex prompts
- Best suited for simple chat and experimentation, not production use
- The "250M" branding reflects its model class, actual parameter count is ~362M
About Axon Labs
Axon Labs builds AI models and tools. This is our tiny model — small enough to run anywhere, dumb enough to be funny.
- Total size
- 1.72 GB
- Files
- 9
- Last updated
- Jun 22
- Pre-warmed CDN
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