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Model: prithivMLmods/Galactic-Qwen-14B-Exp2 Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- prithivMLmods/Lacerta-Opus-14B-Elite8
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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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- code
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- trl
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- SFT
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- math
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model-index:
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- name: Galactic-Qwen-14B-Exp2
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: wis-k/instruction-following-eval
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split: train
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 66.2
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||||
name: averaged accuracy
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source:
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url: >-
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||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
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name: Open LLM Leaderboard
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||||
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: SaylorTwift/bbh
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split: test
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args:
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num_few_shot: 3
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metrics:
|
||||
- type: acc_norm
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||||
value: 59.92
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||||
name: normalized accuracy
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||||
source:
|
||||
url: >-
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||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: lighteval/MATH-Hard
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split: test
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args:
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num_few_shot: 4
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metrics:
|
||||
- type: exact_match
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value: 34.74
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||||
name: exact match
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||||
source:
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||||
url: >-
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||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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split: train
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args:
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num_few_shot: 0
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metrics:
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||||
- type: acc_norm
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||||
value: 19.91
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||||
name: acc_norm
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||||
source:
|
||||
url: >-
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||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
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name: Open LLM Leaderboard
|
||||
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
|
||||
- type: acc_norm
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||||
value: 28.49
|
||||
name: acc_norm
|
||||
source:
|
||||
url: >-
|
||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
|
||||
name: Open LLM Leaderboard
|
||||
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
|
||||
- type: acc
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||||
value: 52.12
|
||||
name: accuracy
|
||||
source:
|
||||
url: >-
|
||||
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FGalactic-Qwen-14B-Exp2
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name: Open LLM Leaderboard
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||||
---
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||||

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# **Galactic-Qwen-14B-Exp2**
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Galactic-Qwen-14B-Exp2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
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<details>
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<summary>BBH (3-Shot) Text Generation Benchmark</summary>
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- [View Leaderboard on Papers with Code](https://paperswithcode.com/sota/text-generation-on-bbh-3-shot)
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</details>
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## **Key Improvements**
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1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.
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2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.
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3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.
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4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
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5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
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||||
|
||||

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## **Quickstart with transformers**
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||||
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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||||
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model_name = "prithivMLmods/Galactic-Qwen-14B-Exp2"
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model = AutoModelForCausalLM.from_pretrained(
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||||
model_name,
|
||||
torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "What are the key principles of general-purpose AI?"
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messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
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||||
{"role": "user", "content": prompt}
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||||
]
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||||
text = tokenizer.apply_chat_template(
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||||
messages,
|
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tokenize=False,
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||||
add_generation_prompt=True
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||||
)
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||||
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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||||
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||||
generated_ids = model.generate(
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||||
**model_inputs,
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max_new_tokens=512
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||||
)
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||||
generated_ids = [
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||||
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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||||
]
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||||
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||||
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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||||
```
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||||
## **Intended Use**
|
||||
1. **General-Purpose Reasoning**:
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||||
Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.
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||||
|
||||
2. **Educational and Informational Assistance**:
|
||||
Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.
|
||||
|
||||
3. **Conversational AI and Chatbots**:
|
||||
Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.
|
||||
|
||||
4. **Multilingual Applications**:
|
||||
Supports global communication, translations, and multilingual content generation.
|
||||
|
||||
5. **Structured Data Processing**:
|
||||
Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.
|
||||
|
||||
6. **Long-Form Content Generation**:
|
||||
Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.
|
||||
|
||||
## **Limitations**
|
||||
1. **Hardware Requirements**:
|
||||
Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.
|
||||
|
||||
2. **Potential Bias in Responses**:
|
||||
While designed to be neutral, outputs may still reflect biases present in training data.
|
||||
|
||||
3. **Inconsistent Outputs in Creative Tasks**:
|
||||
May produce variable results in storytelling and highly subjective topics.
|
||||
|
||||
4. **Limited Real-World Awareness**:
|
||||
Does not have access to real-time events beyond its training cutoff.
|
||||
|
||||
5. **Error Propagation in Extended Outputs**:
|
||||
Minor errors in early responses may affect overall coherence in long-form outputs.
|
||||
|
||||
6. **Prompt Sensitivity**:
|
||||
The effectiveness of responses may depend on how well the input prompt is structured.
|
||||
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
|
||||
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Galactic-Qwen-14B-Exp2-details)!
|
||||
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FGalactic-Qwen-14B-Exp2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
|
||||
|
||||
| Metric |Value (%)|
|
||||
|-------------------|--------:|
|
||||
|**Average** | 43.56|
|
||||
|IFEval (0-Shot) | 66.20|
|
||||
|BBH (3-Shot) | 59.92|
|
||||
|MATH Lvl 5 (4-Shot)| 34.74|
|
||||
|GPQA (0-shot) | 19.91|
|
||||
|MuSR (0-shot) | 28.49|
|
||||
|MMLU-PRO (5-shot) | 52.12|
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||||
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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"max_position_embeddings": 131072,
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"max_window_layers": 70,
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"model_type": "qwen2",
|
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
|
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.49.0",
|
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"use_cache": false,
|
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"use_sliding_window": false,
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}
|
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
|
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"transformers_version": "4.49.0"
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}
|
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"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Virtuoso Small, created by Arcee AI. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user