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Model: prithivMLmods/Kapteyn-500M
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+ 
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/EaSsXHEv3KS2hMQWJ7mEf.png)
+
+# **Kapteyn-500M**
+
+> **Kapteyn-500M** is a lightweight, general-purpose micro language model based on the **LlamaForCausalLM architecture** and trained on the **Llama2 Group of models**. This compact 500M parameter model is designed for **simple chats and responses**, making it ideal for conversational AI applications where efficiency and quick response times are prioritized over complex reasoning tasks.
+
+---
+
+## **Key Features**
+
+1. **Compact & Efficient Architecture**
+   Built on the proven **LlamaForCausalLM architecture** with only 500M parameters, ensuring fast inference and low memory footprint for resource-constrained environments.
+
+2. **General-Purpose Conversational AI**
+   Optimized for natural dialogue, casual conversations, and simple Q&A tasks—perfect for chatbots, virtual assistants, and interactive applications.
+
+3. **Llama2-Based Training**
+   Leverages the robust foundation of the **Llama2 Group of models**, inheriting their conversational capabilities while maintaining ultra-lightweight deployment requirements.
+
+4. **Fast Response Generation**
+   Designed for quick inference with minimal latency, making it suitable for real-time chat applications and interactive user experiences.
+
+5. **Versatile Deployment Options**
+   Runs efficiently on **CPUs**, **entry-level GPUs**, **mobile devices**, and **edge computing platforms** with minimal resource requirements.
+
+6. **Simple Integration**
+   Easy to integrate into existing applications with standard transformer interfaces and minimal setup requirements.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Kapteyn-500M"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Hello! How are you doing today?"
+
+messages = [
+    {"role": "system", "content": "You are a helpful and friendly assistant."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* Casual conversation and general chat applications
+* Simple Q&A systems and customer service bots
+* Educational tools requiring basic conversational interaction
+* Mobile and edge AI applications with limited computational resources
+* Prototyping conversational AI features before scaling to larger models
+* Personal assistants for everyday tasks and simple information retrieval
+
+---
+
+## **Limitations**
+
+* Limited complex reasoning and analytical capabilities compared to larger models
+* Not suitable for specialized technical, scientific, or mathematical tasks
+* Context window limitations may affect longer conversations
+* May struggle with nuanced or highly specialized domain knowledge
+* Optimized for simple responses rather than detailed explanations or complex problem-solving.

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% if ((message['role'] == 'user') != (loop.index0 % 2 == 0)) or ((message['role'] == 'assistant') != (loop.index0 % 2 == 1)) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<|prompt|>' + message['content'].strip() + eos_token }}{% elif message['role'] == 'assistant' %}{{ '<|answer|>' + message['content'].strip() + eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|answer|>' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 96,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 8192,
+  "pad_token_id": 0,
+  "transformers_version": "4.52.4"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "</s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 8192,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}