Quantization made by Richard Erkhov.
Mixtral_7Bx2_MoE - GGUF
- Model creator: https://huggingface.co/cloudyu/
- Original model: https://huggingface.co/cloudyu/Mixtral_7Bx2_MoE/
| Name | Quant method | Size |
|---|---|---|
| Mixtral_7Bx2_MoE.Q2_K.gguf | Q2_K | 4.43GB |
| Mixtral_7Bx2_MoE.IQ3_XS.gguf | IQ3_XS | 4.95GB |
| Mixtral_7Bx2_MoE.IQ3_S.gguf | IQ3_S | 5.22GB |
| Mixtral_7Bx2_MoE.Q3_K_S.gguf | Q3_K_S | 5.2GB |
| Mixtral_7Bx2_MoE.IQ3_M.gguf | IQ3_M | 5.35GB |
| Mixtral_7Bx2_MoE.Q3_K.gguf | Q3_K | 5.78GB |
| Mixtral_7Bx2_MoE.Q3_K_M.gguf | Q3_K_M | 5.78GB |
| Mixtral_7Bx2_MoE.Q3_K_L.gguf | Q3_K_L | 6.27GB |
| Mixtral_7Bx2_MoE.IQ4_XS.gguf | IQ4_XS | 6.5GB |
| Mixtral_7Bx2_MoE.Q4_0.gguf | Q4_0 | 6.78GB |
| Mixtral_7Bx2_MoE.IQ4_NL.gguf | IQ4_NL | 6.85GB |
| Mixtral_7Bx2_MoE.Q4_K_S.gguf | Q4_K_S | 6.84GB |
| Mixtral_7Bx2_MoE.Q4_K.gguf | Q4_K | 7.25GB |
| Mixtral_7Bx2_MoE.Q4_K_M.gguf | Q4_K_M | 7.25GB |
| Mixtral_7Bx2_MoE.Q4_1.gguf | Q4_1 | 7.52GB |
| Mixtral_7Bx2_MoE.Q5_0.gguf | Q5_0 | 8.26GB |
| Mixtral_7Bx2_MoE.Q5_K_S.gguf | Q5_K_S | 8.26GB |
| Mixtral_7Bx2_MoE.Q5_K.gguf | Q5_K | 8.51GB |
| Mixtral_7Bx2_MoE.Q5_K_M.gguf | Q5_K_M | 8.51GB |
| Mixtral_7Bx2_MoE.Q5_1.gguf | Q5_1 | 9.01GB |
| Mixtral_7Bx2_MoE.Q6_K.gguf | Q6_K | 9.84GB |
| Mixtral_7Bx2_MoE.Q8_0.gguf | Q8_0 | 12.75GB |
Original model description:
license: cc-by-nc-4.0 model-index: - name: Mixtral_7Bx2_MoE 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: 71.25 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE 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: 87.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE 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: 64.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE 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: 67.23 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE 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: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE 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: 68.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_7Bx2_MoE name: Open LLM Leaderboard
Mixtral MOE 2x7B
MoE of the following models :
metrics: Average 73.43 ARC 71.25 HellaSwag 87.45
gpu code example
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx2_MoE"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
CPU example
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx2_MoE"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 73.43 |
| AI2 Reasoning Challenge (25-Shot) | 71.25 |
| HellaSwag (10-Shot) | 87.45 |
| MMLU (5-Shot) | 64.98 |
| TruthfulQA (0-shot) | 67.23 |
| Winogrande (5-shot) | 81.22 |
| GSM8k (5-shot) | 68.46 |