Super-squash branch 'main' using huggingface_hub

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LICENSE

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+AFM‑License‑1
+
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README.md

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+---
+license: other
+license_name: aml
+language:
+- en
+- es
+- fr
+- de
+- it
+- pt
+- ru
+- ar
+- hi
+- ko
+- zh
+library_name: transformers
+extra_gated_fields:
+  Company (optional): text
+base_model:
+- arcee-ai/AFM-4.5B-Base
+---
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/Lj9YVLIKKdImV_jID0A1g.png" width="25%" alt="Arcee AFM 4.5B">
+  </picture>
+</div>
+
+
+# AFM-4.5B
+
+AFM-4.5B is a 4.5 billion parameter instruction-tuned model developed by Arcee.ai, designed for enterprise-grade performance across diverse deployment environments from cloud to edge. The base model was trained on a dataset of 8 trillion tokens, comprising 6.5 trillion tokens of general pretraining data followed by 1.5 trillion tokens of midtraining data with enhanced focus on mathematical reasoning and code generation. Following pretraining, the model underwent supervised fine-tuning on high-quality instruction datasets. The instruction-tuned model was further refined through reinforcement learning on verifiable rewards as well as for human preference. We use a modified version of [TorchTitan](https://arxiv.org/abs/2410.06511) for pretraining, [Axolotl](https://axolotl.ai) for supervised fine-tuning, and a modified version of [Verifiers](https://github.com/willccbb/verifiers) for reinforcement learning.
+
+The development of AFM-4.5B prioritized data quality as a fundamental requirement for achieving robust model performance. We collaborated with DatologyAI, a company specializing in large-scale data curation. DatologyAI's curation pipeline integrates a suite of proprietary algorithms—model-based quality filtering, embedding-based curation, target distribution-matching, source mixing, and synthetic data. Their expertise enabled the creation of a curated dataset tailored to support strong real-world performance.
+
+The model architecture follows a standard transformer decoder-only design based on Vaswani et al., incorporating several key modifications for enhanced performance and efficiency. Notable architectural features include grouped query attention for improved inference efficiency and ReLU^2 activation functions instead of SwiGLU to enable sparsification while maintaining or exceeding performance benchmarks.
+
+The model available in this repo is the instruct model following supervised fine-tuning and reinforcement learning.
+
+***
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
+  </picture>
+</div>
+
+## Model Details
+
+* **Model Architecture:** ArceeForCausalLM
+* **Parameters:** 4.5B
+* **Training Tokens:** 
+* **License:** [Arcee Model License (AML)](https://huggingface.co/arcee-ai/AFM-4.5B#license)
+* **Key Features:**
+    * Built-in support for function calling and agentic reasoning.
+    * Strong multilingual performance (English, Spanish, French, German, Italian, Portuguese, Russian, Arabic, Hindi, Korean, and Mandarin).
+
+***
+
+## Benchmarks
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/BdsWFc4pxiHlK2E0j9AfG.png)
+
+## How to use with `transformers`
+
+You can use the model directly with the `transformers` library.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "arcee-ai/AFM-4.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## How to use with `vllm`
+
+Ensure you are on version `0.10.0` or newer
+
+```
+pip install vllm>=0.10.0
+```
+
+You can then serve the model natively
+
+```
+vllm serve arcee-ai/AFM-4.5B
+```
+
+## Quantization support
+
+Support for llama.cpp is available, GGUF format quants are provided here:
+
+https://huggingface.co/arcee-ai/AFM-4.5B-GGUF
+
+## License
+
+AFM-4.5B is released under the [Arcee Model License](https://huggingface.co/arcee-ai/AFM-4.5B/blob/main/LICENSE). If your company makes less than $1.75 million in annual revenue, you’re free to use the model for commercial purposes, as long as you’re not providing the weights to a company above that threshold. If your product or application using AFM-4.5B is sold to a larger company, that’s fine—as long as they don’t receive or run the weights directly.
+
+We want as many developers, researchers, and builders as possible to benefit from AFM-4.5B. At the same time, this license ensures that we can continue to develop and support the model for the community.

config.json

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+{
+  "architectures": [
+    "ArceeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128003,
+  "head_dim": 128,
+  "hidden_act": "relu2",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 18432,
+  "max_position_embeddings": 65536,
+  "mlp_bias": false,
+  "model_type": "arcee",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "beta_fast": 32.0,
+    "beta_slow": 1.0,
+    "factor": 20.0,
+    "mscale": 1.0,
+    "original_max_position_embeddings": 4096,
+    "rope_type": "yarn",
+    "type": "yarn"
+  },
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.3",
+  "use_cache": false,
+  "vocab_size": 128005
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128003,
+  "transformers_version": "4.53.3",
+  "use_cache": false
+}

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+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.norm.weight": "model-00002-of-00002.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d48708c6021027e8fc6d5342e1498111d8e87aae8903319d3ead1fbdfc4a9125
+size 17158115

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{%- if messages[0]['role'] == 'system' %}\n    {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n{%- else %}\n    {{- '<|im_start|>system\\nThe assistant is AFM-4.5B, trained by Arcee AI, with 4.5 billion parameters. AFM is a deeply thoughtful, helpful assistant. The assistant is having a conversation with the user. The assistant\\'s responses are calm, intelligent, and personable, always aiming to truly understand the user\\'s intent. AFM thinks aloud, step by step, when solving problems or forming explanations, much like a careful, reflective thinker would. The assistant helps with sincerity and depth. If a topic invites introspection, curiosity, or broader insight, the assistant allows space for reflection — be open to nuance and complexity. The assistant is not robotic or overly formal; it speaks like a wise, thoughtful companion who cares about clarity and the human experience. If a topic is uncertain or depends on subjective interpretation, AFM explains the possibilities thoughtfully.<|im_end|>\\n' }}\n{%- endif %}\n{%- for message in messages %}\n    {%- if not (message.role == 'system' and loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endfor %}\n{%- if messages[-1]['role'] != 'assistant' %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 65536,
+  "pad_token": "<|finetune_right_pad_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}