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Model: axonlabsai/axon-250m
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation
+- small-model
+- tiny-model
+- chat
+- axon
+- custom-model
+- llama
+- not-finetuned
+base_model: HuggingFaceTB/SmolLM2-360M
+---
+
+# 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
+
+```python
+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.

axon-250m-Q4_K_M.gguf

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chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_parameters": {
+    "rope_theta": 100000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "is_local": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}