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RichardErkhov/
M4-ai_-_tau-1.8B-gguf

Name: M4-ai_-_tau-1.8B-gguf
Availability: InStock

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tau-1.8B - GGUF

Model creator: https://huggingface.co/M4-ai/
Original model: https://huggingface.co/M4-ai/tau-1.8B/

Name	Quant method	Size
tau-1.8B.Q2_K.gguf	Q2_K	0.79GB
tau-1.8B.Q3_K_S.gguf	Q3_K_S	0.89GB
tau-1.8B.Q3_K.gguf	Q3_K	0.95GB
tau-1.8B.Q3_K_M.gguf	Q3_K_M	0.95GB
tau-1.8B.Q3_K_L.gguf	Q3_K_L	0.98GB
tau-1.8B.IQ4_XS.gguf	IQ4_XS	1.01GB
tau-1.8B.Q4_0.gguf	Q4_0	1.04GB
tau-1.8B.IQ4_NL.gguf	IQ4_NL	1.05GB
tau-1.8B.Q4_K_S.gguf	Q4_K_S	1.08GB
tau-1.8B.Q4_K.gguf	Q4_K	1.13GB
tau-1.8B.Q4_K_M.gguf	Q4_K_M	1.13GB
tau-1.8B.Q4_1.gguf	Q4_1	1.13GB
tau-1.8B.Q5_0.gguf	Q5_0	1.22GB
tau-1.8B.Q5_K_S.gguf	Q5_K_S	1.24GB
tau-1.8B.Q5_K.gguf	Q5_K	1.28GB
tau-1.8B.Q5_K_M.gguf	Q5_K_M	1.28GB
tau-1.8B.Q5_1.gguf	Q5_1	1.31GB
tau-1.8B.Q6_K.gguf	Q6_K	1.47GB
tau-1.8B.Q8_0.gguf	Q8_0	1.82GB

Original model description:

language:

en
zh license: other datasets:
Locutusque/UltraTextbooks-2.0 license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE inference: parameters: do_sample: true temperature: 0.8 top_p: 0.95 top_k: 40 max_new_tokens: 250 repetition_penalty: 1.1 model-index:
name: tau-1.8B results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 37.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 60.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 45.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 39.72 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 61.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 30.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/tau-1.8B name: Open LLM Leaderboard

tau-1.8B

Model Details

Model Name: tau-1.8B
Base Model: Qwen1.5-1.8B
Dataset: UltraTextbooks-2.0
Model Size: 1.8B parameters
Model Type: Language Model
Training Procedure: Further pre-training of Qwen1.5-1.8B on UltraTextbooks-2.0.

Model Use

tau-1.8B is designed to be a general-purpose language model with enhanced capabilities in the domains of machine learning, mathematics, and coding. It can be used for a wide range of natural language processing tasks, such as:

Educational question answering
Text summarization
Content generation for educational purposes
Code understanding and generation
Mathematical problem solving

The model's exposure to the diverse content in the UltraTextbooks-2.0 dataset makes it particularly well-suited for applications in educational technology and research.

Training Data

tau-1.8B was further pre-trained on the UltraTextbooks-2.0 dataset, which is an expanded version of the original UltraTextbooks dataset. UltraTextbooks-2.0 incorporates additional high-quality synthetic and human-written textbooks from various sources on the Hugging Face platform, with a focus on increasing the diversity of content in the domains of machine learning, mathematics, and coding.

For more details on the dataset, please refer to the UltraTextbooks-2.0 Dataset Card.

Performance and Limitations

Refer to Evaluation for evaluations. It is essential to note that the model may still exhibit biases or inaccuracies present in the training data. Users are encouraged to critically evaluate the model's outputs and report any issues to facilitate continuous improvement.

Environmental Impact

The training of tau-1.8B required computational resources that contribute to the model's overall environmental impact. However, efforts were made to optimize the training process and minimize the carbon footprint.

Ethical Considerations

tau-1.8B was trained on a diverse dataset that may contain biases and inaccuracies. Users should be aware of these potential limitations and use the model responsibly. The model should not be used for tasks that could cause harm or discriminate against individuals or groups.

Evaluation

Metric	Value
Avg.	45.73
AI2 Reasoning Challenge (25-Shot)	37.20
HellaSwag (10-Shot)	60.26
MMLU (5-Shot)	45.96
TruthfulQA (0-shot)	39.72
Winogrande (5-shot)	61.09
GSM8k (5-shot)	30.17

RichardErkhov/M4-ai_-_tau-1.8B-gguf