Improve language tag
Browse filesHi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.
README.md
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license: creativeml-openrail-m
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datasets:
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- amphora/QwQ-LongCoT-130K
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language:
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---
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---
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license: creativeml-openrail-m
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datasets:
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- amphora/QwQ-LongCoT-130K
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- long-CoT
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- safetensors
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- 3B
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- Instruct
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- QwQ
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- Qwen2.5
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---
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### **QwQ-LCoT-3B-Instruct Model Card**
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The **QwQ-LCoT-3B-Instruct** model is a lightweight, instruction-tuned language model designed for complex reasoning and explanation tasks. It is fine-tuned on the **Qwen2.5-3B-Instruct** base model using the **QwQ-LongCoT-130K** dataset, focusing on **long-chain-of-thought (LCoT)** reasoning for enhanced logical comprehension and detailed output generation.
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| **File Name** | **Size** | **Description** | **Upload Status** |
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|----------------------------------------|----------------|-------------------------------------------------|--------------------|
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| `.gitattributes` | 1.57 kB | Specifies LFS tracking for large files. | Uploaded |
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| `README.md` | 267 Bytes | Basic project information file. | Updated |
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| `added_tokens.json` | 657 Bytes | Custom tokens added to the tokenizer. | Uploaded |
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| `config.json` | 859 Bytes | Configuration file for the model. | Uploaded |
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| `generation_config.json` | 281 Bytes | Configuration file for text generation settings.| Uploaded |
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| `merges.txt` | 1.82 MB | Contains the byte-pair encoding (BPE) merges. | Uploaded |
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| `pytorch_model-00001-of-00002.bin` | 4.96 GB | First shard of the model weights in PyTorch format. | Uploaded (LFS) |
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| `pytorch_model-00002-of-00002.bin` | 1.21 GB | Second shard of the model weights in PyTorch format. | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 36 kB | Index mapping for sharded model weights. | Uploaded |
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| `special_tokens_map.json` | 644 Bytes | Maps special tokens to their roles. | Uploaded |
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| `tokenizer.json` | 11.4 MB | Serialized tokenizer data. | Uploaded (LFS) |
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| `tokenizer_config.json` | 7.73 kB | Tokenizer configuration settings. | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary file for the tokenizer. | Uploaded |
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### **Sample Long CoT:**
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### **Key Features:**
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1. **Long Chain-of-Thought Reasoning:**
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- Specifically designed to generate comprehensive, step-by-step explanations for complex queries.
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2. **Lightweight and Efficient:**
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- With only 3 billion parameters, it is optimized for systems with limited computational resources without compromising reasoning capabilities.
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3. **Instruction Optimization:**
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- Fine-tuned to follow prompts and provide concise, actionable, and structured responses.
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---
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### **Training Details:**
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- **Base Model:** [Qwen2.5-3B-Instruct](#)
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- **Dataset:** [amphora/QwQ-LongCoT-130K](#)
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- Comprising 133,000 annotated samples focusing on logical tasks and structured thinking.
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---
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### **Capabilities:**
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1. **Text Generation:**
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- Provides detailed, structured, and logical text outputs tailored to user prompts.
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2. **Reasoning Tasks:**
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- Solves step-by-step problems in math, logic, and science.
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3. **Educational Assistance:**
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- Generates coherent explanations for academic and research purposes.
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4. **Dialogue and Summarization:**
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- Handles conversational queries and summarizes long documents effectively.
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---
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### **Usage Instructions:**
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1. **Setup:**
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Download all model files and ensure compatibility with the Hugging Face Transformers library.
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2. **Loading the Model:**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/QwQ-LCoT-3B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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```
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3. **Generate Long-Chain Reasoning Outputs:**
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```python
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input_text = "Explain the process of photosynthesis step-by-step."
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=300, temperature=0.5)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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4. **Customize Output Generation:**
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Modify the `generation_config.json` file for different scenarios:
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- **`temperature`**: Controls randomness (lower = deterministic, higher = creative).
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- **`max_length`**: Sets response length.
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- **`top_p`**: Adjusts sampling for diversity in outputs.
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---
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