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RichardErkhov/abacusai_-_bigyi-15b-gguf
abacusai_-_bigyi-15b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
86
10% of all-time downloads
All-time downloads
887
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151 GB
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.gguf178 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
bigyi-15b - GGUF
| Name | Quant method | Size |
|---|---|---|
| bigyi-15b.Q2_K.gguf | Q2_K | 5.26GB |
| bigyi-15b.Q3_K_S.gguf | Q3_K_S | 6.13GB |
| bigyi-15b.Q3_K.gguf | Q3_K | 6.82GB |
| bigyi-15b.Q3_K_M.gguf | Q3_K_M | 6.82GB |
| bigyi-15b.Q3_K_L.gguf | Q3_K_L | 7.42GB |
| bigyi-15b.IQ4_XS.gguf | IQ4_XS | 7.62GB |
| bigyi-15b.Q4_0.gguf | Q4_0 | 7.96GB |
| bigyi-15b.IQ4_NL.gguf | IQ4_NL | 8.03GB |
| bigyi-15b.Q4_K_S.gguf | Q4_K_S | 8.01GB |
| bigyi-15b.Q4_K.gguf | Q4_K | 8.43GB |
| bigyi-15b.Q4_K_M.gguf | Q4_K_M | 8.43GB |
| bigyi-15b.Q4_1.gguf | Q4_1 | 8.82GB |
| bigyi-15b.Q5_0.gguf | Q5_0 | 9.68GB |
| bigyi-15b.Q5_K_S.gguf | Q5_K_S | 9.68GB |
| bigyi-15b.Q5_K.gguf | Q5_K | 9.92GB |
| bigyi-15b.Q5_K_M.gguf | Q5_K_M | 9.92GB |
| bigyi-15b.Q5_1.gguf | Q5_1 | 10.54GB |
| bigyi-15b.Q6_K.gguf | Q6_K | 11.51GB |
| bigyi-15b.Q8_0.gguf | Q8_0 | 14.9GB |
base_model:
I recently made bigstral-12b and then I saw this new awesome model yi-9b and decided to make an embiggened version.
This is a merge of pre-trained language models created using mergekit.
Bigyi-15b is a base / completion model, so there is no chat template.
It has a 4k context.
Here is a recipe for Mai Tai:\n\n1:
3 parts rum, 2: 3 parts pineapple juice, 3: half a cup of lime juice, 4: 6 to 8 fresh or frozen pineapple chunks, 5: crushed ice. Mix all ingredients except ice and pour into glasses with ice. Garnish with a pineapple slice.
Here is an implementation of 2-sum in golang:
func twoSum(nums []int, target int) []int { if len(nums) <= 1 { return nil } m := map[int] bool{} for i := range(nums) {{ n = nums[i] // find the complement of current number in map comp = target - n if comp in m { return [m[comp], i+1 ] } else { m[n] = true } }} return nil }
This model was merged using the passthrough merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 12]
model: 01-ai/Yi-9B
- sources:
- layer_range: [6, 18]
model: 01-ai/Yi-9B
- sources:
- layer_range: [12, 24]
model: 01-ai/Yi-9B
- sources:
- layer_range: [18, 30]
model: 01-ai/Yi-9B
- sources:
- layer_range: [24, 36]
model: 01-ai/Yi-9B
- sources:
- layer_range: [30, 42]
model: 01-ai/Yi-9B
- sources:
- layer_range: [36, 48]
model: 01-ai/Yi-9B