4-bit Autotrain compatible Awq Conversational En Endpoints compatible Int4 Llama Pytorch Quantized Region:us Safetensors Sharded Tensorflow Yi1.5-9b-chat Zh
Yi 1.5 9B Chat AWQ Benchmarks
ARC: 63.65 vs 96.7 (so35)-34.2%
HellaSwag: 80.94 vs 95.3 (gpt4)-15.1%
MMLU: 71.01 vs 88.3 (so35)-19.6%
TruthfulQA: 52.67 vs 59 (gpt4)-10.7%
WinoGrande: 77.19 vs 87.5 (gpt4)-11.8%
GSM8K: 71.87 vs 96.4 (so35)-25.4%
LLME Score: 0.22971
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
What is the LLM Explorer Rank (Score)
Yi 1.5 9B Chat AWQ Parameters and Internals
| LLM Name | Yi 1.5 9B Chat AWQ |
| Repository 🤗 | https://huggingface.co/modelscope/Yi-1.5-9B-Chat-AWQ |
| Base Model(s) | |
| Model Size | 9b |
| Required VRAM | 5.3 GB |
| Updated | 2024-09-07 |
| Maintainer | modelscope |
| Model Type | llama |
| Model Files | |
| AWQ Quantization | Yes |
| Quantization Type | awq |
| Model Architecture | LlamaForCausalLM |
| License | apache-2.0 |
| Context Length | 4096 |
| Model Max Length | 4096 |
| Transformers Version | 4.39.3 |
| Tokenizer Class | LlamaTokenizer |
| Padding Token | <unk> |
| Vocabulary Size | 64000 |
| Torch Data Type | float16 |
Best Alternatives to Yi 1.5 9B Chat AWQ
Best Alternatives |
Context / RAM |
Downloads |
Likes |
|---|---|---|---|
| 01 Ai Yi 1.5 9B Chat AWQ | 4K / 5.4 GB | 6 | 0 |
| 01 Ai Yi 1.5 9B AWQ | 4K / 5.4 GB | 5 | 0 |
| Yi 9B 200K 3.0bpw H6 EXL2 | 256K / 3.9 GB | 4 | 1 |
| Yi 9B 200K 6.0bpw H6 EXL2 | 256K / 7 GB | 4 | 1 |
| Faro Yi 9B DPO 6bpw EXL2 | 32K / 7 GB | 4 | 1 |
| Faro Yi 9B DPO 8bpw EXL2 | 32K / 8.3 GB | 4 | 1 |
| Master Yi 9B 8.0bpw H8 EXL2 | 4K / 8.7 GB | 3 | 2 |
| Master Yi 9B 3.0bpw H6 EXL2 | 4K / 3.9 GB | 5 | 1 |
| Master Yi 9B 6.0bpw H6 EXL2 | 4K / 7 GB | 5 | 1 |
| Master Yi 9B 4.0bpw H6 EXL2 | 4K / 4.9 GB | 2 | 1 |
Rank the Yi 1.5 9B Chat AWQ Capabilities
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024072803