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Model: kd13/Type-o1-mini-instruct
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+language:
+- en
+- hi
+base_model:
+- meta-llama/Llama-3.2-1B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- llama
+- mini
+---
+
+# Type-o1-mini-instruct
+
+A compact general-purpose instruct model designed for everyday assistant use across a wide range of domains — from science and math to writing, coding, language tasks, and tool-style web search workflows.
+
+The model is intended for lightweight assistant use cases where users need clear, well-structured answers, helpful explanations, and practical support across many subject areas.
+
+## Capabilities
+
+This model can help with:
+
+* General chat and multi-turn conversation
+* Biology, chemistry, and physics questions and explanations
+* Mathematics and quantitative reasoning
+* Engineering concepts and explanations
+* Health and medical information (general, non-clinical)
+* Python coding assistance and code explanation
+* Creative writing (stories, poetry, writing prompts)
+* Content generation (marketing copy, social media captions, emails)
+* English grammar correction and rewriting
+* Advanced NLP tasks:
+  * Fill-mask
+  * Table question answering
+  * Context-based question answering (SQuAD style)
+  * Summarization (dialogue, news, and scientific papers)
+* English ↔ Hindi translation
+* School and coursework-level question answering
+* Web search tool-call style conversations
+
+## Chat Format
+
+The model follows a Harmony-style chat structure.
+
+Supported interaction flow:
+
+```text
+system -> developer -> user -> tool call -> tool result -> final response
+```
+
+For normal chat use, you can use a standard chat-template style prompt.
+
+## Web Search Tool-Call Style
+
+The model can be used in tool-calling style conversations where the assistant decides when a search is needed, emits a tool call, receives a tool result, and then writes the final answer.
+
+Example structure:
+
+```text
+system: You are a helpful assistant with access to web search.
+user: Find the latest information about a topic.
+assistant tool call: web_search(...)
+tool result: ...
+assistant final: Answer using the search result.
+```
+
+Actual tool execution depends on your inference framework or application wrapper.
+
+## Recommended Use Cases
+
+This model is best suited for:
+
+* General-purpose lightweight assistants
+* Study and homework helpers across science subjects
+* Writing and content generation helpers
+* Grammar and language correction tools
+* English ↔ Hindi translation helpers
+* Summarization and document Q&A tools
+* Beginner Python learning assistants
+* Tool-call research experiments
+* Chatbots that need broad domain coverage in a small model
+
+## Limitations
+
+This model is not recommended for:
+
+* Production-critical software generation without review
+* Non-Python coding tasks such as C++, Java, Rust, Go, or JavaScript
+* Security-sensitive code generation
+* Medical, legal, or financial decision-making
+* Advanced research-level science or mathematics
+* Long multi-file software engineering tasks
+* Tasks requiring very long context
+* High-stakes factual lookup without verification
+
+The model may sometimes:
+
+* Produce incorrect facts or reasoning
+* Miss edge cases
+* Over-explain simple questions
+* Generate code that needs testing
+* Struggle with very long context
+* Use tool-call format inconsistently depending on the prompt
+* Give uneven quality across its many supported domains
+
+Always verify important outputs and test generated code before using it.
+
+## License
+
+Please check the model repository license before commercial or production use.
+
+## Disclaimer
+
+This model is an experimental small general-purpose assistant. It should be used as a helpful assistant, not as a guaranteed source of truth. For important tasks, verify outputs with tests, documentation, and human review.

chat_template.jinja

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+{% for m in messages %}<|begin_of_text|>{{ m['role'] }}
+{{ m['content'] }}<|end_of_text|>
+{% endfor %}{% if add_generation_prompt %}<|begin_of_text|>assistant
+{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.6"
+}

model.safetensors

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+size 2471645608

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|end_of_text|>"
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "TokenizersBackend"
+}