Quantization made by Richard Erkhov.
Cinder-1.3B-Test - GGUF
- Model creator: https://huggingface.co/Josephgflowers/
- Original model: https://huggingface.co/Josephgflowers/Cinder-1.3B-Test/
Name | Quant method | Size |
---|---|---|
Cinder-1.3B-Test.Q2_K.gguf | Q2_K | 0.46GB |
Cinder-1.3B-Test.IQ3_XS.gguf | IQ3_XS | 0.51GB |
Cinder-1.3B-Test.IQ3_S.gguf | IQ3_S | 0.54GB |
Cinder-1.3B-Test.Q3_K_S.gguf | Q3_K_S | 0.54GB |
Cinder-1.3B-Test.IQ3_M.gguf | IQ3_M | 0.56GB |
Cinder-1.3B-Test.Q3_K.gguf | Q3_K | 0.59GB |
Cinder-1.3B-Test.Q3_K_M.gguf | Q3_K_M | 0.59GB |
Cinder-1.3B-Test.Q3_K_L.gguf | Q3_K_L | 0.64GB |
Cinder-1.3B-Test.IQ4_XS.gguf | IQ4_XS | 0.66GB |
Cinder-1.3B-Test.Q4_0.gguf | Q4_0 | 0.69GB |
Cinder-1.3B-Test.IQ4_NL.gguf | IQ4_NL | 0.69GB |
Cinder-1.3B-Test.Q4_K_S.gguf | Q4_K_S | 0.69GB |
Cinder-1.3B-Test.Q4_K.gguf | Q4_K | 0.72GB |
Cinder-1.3B-Test.Q4_K_M.gguf | Q4_K_M | 0.72GB |
Cinder-1.3B-Test.Q4_1.gguf | Q4_1 | 0.76GB |
Cinder-1.3B-Test.Q5_0.gguf | Q5_0 | 0.83GB |
Cinder-1.3B-Test.Q5_K_S.gguf | Q5_K_S | 0.83GB |
Cinder-1.3B-Test.Q5_K.gguf | Q5_K | 0.85GB |
Cinder-1.3B-Test.Q5_K_M.gguf | Q5_K_M | 0.85GB |
Cinder-1.3B-Test.Q5_1.gguf | Q5_1 | 0.9GB |
Cinder-1.3B-Test.Q6_K.gguf | Q6_K | 0.98GB |
Cinder-1.3B-Test.Q8_0.gguf | Q8_0 | 1.26GB |
Original model description:
license: mit library_name: transformers base_model: Josephgflowers/TinyLlama-3T-Cinder-v1.2 pipeline_tag: text-generation widget:
text: '<|system|>
You are a helpful assistant
<|user|>
Tell me about yourself, what is your name?.
<|assistant|>'
model-index: - name: Cinder-1.3B-Test 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: 33.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test 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: 55.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test 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: 26.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test 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: 36.62 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test 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: 58.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test 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: 2.81 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test name: Open LLM Leaderboard
Model Card for Model ID
Model Details
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
- PEFT 0.7.1
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 35.57 |
AI2 Reasoning Challenge (25-Shot) | 33.19 |
HellaSwag (10-Shot) | 55.48 |
MMLU (5-Shot) | 26.37 |
TruthfulQA (0-shot) | 36.62 |
Winogrande (5-shot) | 58.96 |
GSM8k (5-shot) | 2.81 |
- Downloads last month
- 193